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|---|---|---|---|---|
1,594 | 1 | Title: An Evolutionary Approach to Vector Quantizer Design
Abstract: Vector quantization is a lossy coding technique for encoding a set of vectors from different sources such as image and speech. The design of vector quantizers that yields the lowest distortion is one of the most challenging problems in the field of source coding. However, this problem is known to be difficult [3]. The conventional solution technique works through a process of iterative refinements which yield only locally optimal results. In this paper, we design and evaluate three versions of genetic algorithms for computing vector quantizers. Our preliminary study with Gaussian-Markov sources showed that the genetic approach outperforms the conventional technique in most cases. | [
163,
1136
] | Train |
1,595 | 6 | Title: ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees
Abstract: We discuss an approach to constructing composite features during the induction of decision trees. The composite features correspond to m-of-n concepts. There are three goals of this research. First, we explore a family of greedy methods for building m-of-n concepts (one of which, GS, is described in this paper). Second, we show how these concepts can be formed as internal nodes of decision trees, serving as a bias to the learner. Finally, we evaluate the method on several artificially generated and naturally occurring data sets to determine the effects of this bias. | [
151,
836,
1301,
1576,
1657,
1776,
1824,
1862,
1863,
1964,
2346,
2675
] | Train |
1,596 | 5 | Title: First Order Regression
Abstract: We present a new approach, called First Order Regression (FOR), to handling numerical information in Inductive Logic Programming (ILP). FOR is a combination of ILP and numerical regression. First-order logic descriptions are induced to carve out those subspaces that are amenable to numerical regression among real-valued variables. The program Fors is an implementation of this idea, where numerical regression is focused on a distinguished continuous argument of the target predicate. We show that this can be viewed as a generalisation of the usual ILP problem. Applications of Fors on several real-world data sets are described: the prediction of mutagenicity of chemicals, the modelling of liquid dynamics in a surge tank, predicting the roughness in steel grinding, finite element mesh design, and operator's skill reconstruction in electric discharge machining. A comparison of Fors' performance with previous results in these domains indicates that Fors is an effective tool for ILP applications that involve numerical data. | [
314,
348,
1244
] | Test |
1,597 | 0 | Title: an Opportunistic Enterprise
Abstract: Tech Report GIT-COGSCI-97/04 Abstract This paper identifies goal handling processes that begin to account for the kind of processes involved in invention. We identify new kinds of goals with special properties and mechanisms for processing such goals, as well as means of integrating opportunism, deliberation, and social interaction into goal/plan processes. We focus on invention goals, which address significant enterprises associated with an inventor. Invention goals represent seed goals of an expert, around which the whole knowledge of an expert gets reorganized and grows more or less opportunistically. Invention goals reflect the idiosyncrasy of thematic goals among experts. They constantly increase the sensitivity of individuals for particular events that might contribute to their satisfaction. Our exploration is based on a well-documented example: the invention of the telephone by Alexander Graham Bell. We propose mechanisms to explain: (1) how Bell's early thematic goals gave rise to the new goals to invent the multiple telegraph and the telephone, and (2) how the new goals interacted opportunistically. Finally, we describe our computational model, ALEC, that accounts for the role of goals in invention. | [
486,
1138,
1148,
1534
] | Train |
1,598 | 1 | Title: Mutation Rates as Adaptations
Abstract: In order to better understand life, it is helpful to look beyond the envelop of life as we know it. A simple model of coevolution was implemented with the addition of a gene for the mutation rate of the individual. This allowed the mutation rate itself to evolve in a lineage. The model shows that when the individuals interact in a sort of zero-sum game, the lineages maintain relatively high mutation rates. However, when individuals engage in interactions that have greater consequences for one individual in the interaction than the other, lineages tend to evolve relatively low mutation rates. This model suggests that different genes may have evolved different mutation rates as adaptations to the varying pressures of interactions with other genes. | [
780,
1139,
1572
] | Train |
1,599 | 4 | Title: Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to
Abstract: In many learning tasks, data-query is neither free nor of constant cost. Often the cost of a query depends on the distance from the current location in state space to the desired query point. This is easiest to visualize in robotics environments where a robot must physically move to a location in order to learn something there. The cost of this learning is the time and effort it takes to reach the new location. Furthermore, this cost is characterized by a distance relationship: When the robot moves as directly as possible from a source state to a destination state, the states through which it passes are closer (i.e., cheaper to reach) than is the destination state. Distance relationships hold in many real-world non-robotics tasks also | any environment where states are not immediately accessible. Optimiz- ing the performance of a chemical plant, for example, requires the adjustment of analog controls which have a continuum of intermediate states. Querying possibly optimal regions of state space in these environments is inadvisable if the path to the query point intersects a region of known volatility. In discrete environments with small numbers of states, it's possible to keep track of precisely where and to what degree learning has already been done sufficiently and where it still needs to be done. It is also possible to keep best known estimates of the distances from each state to each other (see Kaelbling, 1993). Kael- bling's DG-learning algorithm is based on Floyd's all- pairs shortest-path algorithm (Aho, Hopcroft, & Ull- man 1983) and is just slightly different from that used here. These "all-goals" algorithms (after Kaelbling) can provide a highly satisfying representation of the distance/benefit tradeoff. where E x is the exploration value of state x (the potential benefit of exploring state x), D xy is the distance to state y, and A xy is the action to take in state x to move most cheaply to state y. This information can be learned incrementally and completely : That is, it can be guaranteed that if a path from any state x to any state y is deducible from the state transitions seen so far, then (1) the algorithm will have a non-null entry for S xy (i.e., the algorithm will know a path from x to y), and (2) The current value for D xy will be the best deducible value from all data seen so far. With this information, decisions about which areas to explore next can be based on not just the amount to be gained from such exploration but also on the cost of reaching each area together with the benefit of incidental exploration done on the way. Though optimal exploration is NP-hard (i.e., it's at least as difficult as TSP) good approximations are easily computable. One such good approximation is to take the action at each state that leads in the direction of greatest accumulated exploration benefit: | [
671,
1697
] | Train |
1,600 | 2 | Title: First-Order vs. Second-Order Single Layer Recurrent Neural Networks
Abstract: We examine the representational capabilities of first-order and second-order Single Layer Recurrent Neural Networks (SLRNNs) with hard-limiting neurons. We show that a second-order SLRNN is strictly more powerful than a first-order SLRNN. However, if the first-order SLRNN is augmented with output layers of feedforward neurons, it can implement any finite-state recognizer, but only if state-splitting is employed. When a state is split, it is divided into two equivalent states. The judicious use of state-splitting allows for efficient implementation of finite-state recognizers using augmented first-order SLRNNs. | [
411,
946,
1293
] | Validation |
1,601 | 2 | Title: Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models
Abstract: Learning the past tense of English verbs | a seemingly minor aspect of language acquisition | has generated heated debates since 1986, and has become a landmark task for testing the adequacy of cognitive modeling. Several artificial neural networks (ANNs) have been implemented, and a challenge for better symbolic models has been posed. In this paper, we present a general-purpose Symbolic Pattern Associator (SPA) based upon the decision-tree learning algorithm ID3. We conduct extensive head-to-head comparisons on the generalization ability between ANN models and the SPA under different representations. We conclude that the SPA generalizes the past tense of unseen verbs better than ANN models by a wide margin, and we offer insights as to why this should be the case. We also discuss a new default strategy for decision-tree learning algorithms. | [
224,
1155,
1428,
1429,
1644,
2423
] | Train |
1,602 | 3 | Title: Defining Explanation in Probabilistic Systems
Abstract: As probabilistic systems gain popularity and are coming into wider use, the need for a mechanism that explains the system's findings and recommendations becomes more critical. The system will also need a mechanism for ordering competing explanations. We examine two representative approaches to explanation in the literature one due to G ardenfors and one due to Pearland show that both suffer from significant problems. We propose an approach to defining a notion of better explanation that combines some of the features of both together with more recent work by Pearl and others on causality. | [
339,
971,
1326,
1549
] | Train |
1,603 | 1 | Title: Evolving Complex Structures via Co- operative Coevolution
Abstract: A cooperative coevolutionary approach to learning complex structures is presented which, although preliminary in nature, appears to have a number of advantages over non-coevolutionary approaches. The cooperative coevolutionary approach encourages the parallel evolution of substructures which interact in useful ways to form more complex higher level structures. The architecture is designed to be general enough to permit the inclusion, if appropriate, of a priori knowledge in the form of initial biases towards particular kinds of decompositions. A brief summary of initial results obtained from testing this architecture in several problem domains is presented which shows a significant speedup over more traditional non-coevolutionary approaches. | [
1114,
1117,
2089
] | Train |
1,604 | 2 | Title: Analytic Comparison of Nonlinear H 1 -Norm Bounding Techniques for Low Order Systems with Saturation
Abstract: A cooperative coevolutionary approach to learning complex structures is presented which, although preliminary in nature, appears to have a number of advantages over non-coevolutionary approaches. The cooperative coevolutionary approach encourages the parallel evolution of substructures which interact in useful ways to form more complex higher level structures. The architecture is designed to be general enough to permit the inclusion, if appropriate, of a priori knowledge in the form of initial biases towards particular kinds of decompositions. A brief summary of initial results obtained from testing this architecture in several problem domains is presented which shows a significant speedup over more traditional non-coevolutionary approaches. | [
1272,
1281,
1451
] | Train |
1,605 | 6 | Title: Learning from the Environment by Experimentation: The Need for Few and Informative Examples
Abstract: An intelligent system must be able to adapt and learn to correct and update its model of the environment incrementally and deliberately. In complex environments that have many parameters and where interactions have a cost, sampling the possible range of states to test the results of action executions is not a practical approach. We present a practical approach based on continuous and selective interaction with the environment that pinpoints the type of fault in the domain knowledge that causes any unexpected behavior of the environment, and resorts to experimentation when additional information is needed to correct the system's knowledge. | [
1491
] | Test |
1,606 | 2 | Title: Pruning Recurrent Neural Networks for Improved Generalization Performance
Abstract: Determining the architecture of a neural network is an important issue for any learning task. For recurrent neural networks no general methods exist that permit the estimation of the number of layers of hidden neurons, the size of layers or the number of weights. We present a simple pruning heuristic which significantly improves the generalization performance of trained recurrent networks. We illustrate this heuristic by training a fully recurrent neural network on positive and negative strings of a regular grammar. We also show that if rules are extracted from networks trained to recognize these strings, that rules extracted after pruning are more consistent with the rules to be learned. This performance improvement is obtained by pruning and retraining the networks. Simulations are shown for training and pruning a recurrent neural net on strings generated by two regular grammars, a randomly-generated 10-state grammar and an 8-state triple parity grammar. Further simulations indicate that this pruning method can gives generalization performance superior to that obtained by training with weight decay. | [
28,
409,
826,
1293,
2381
] | Train |
1,607 | 6 | Title: Characterizing the generalization performance of model selection strategies
Abstract: We investigate the structure of model selection problems via the bias/variance decomposition. In particular, we characterize the essential aspects of a model selection task by the bias and variance profiles it generates over the sequence of hypothesis classes. With this view, we develop a new understanding of complexity-penalization methods: First, the penalty terms can be interpreted as postulating a particular profile for the variances as a function of model complexityif the postulated and true profiles do not match, then systematic under-fitting or over-fitting results, depending on whether the penalty terms are too large or too small. Second, we observe that it is generally best to penalize according to the true variances of the task, and therefore no fixed penalization strategy is optimal across all problems. We then use this characterization to introduce the notion of easy versus hard model selection problems. Here we show that if the variance profile grows too rapidly in relation to the biases, then standard model selection techniques become prone to significant errors. This can happen, for example, in regression problems where the independent variables are drawn from wide-tailed distributions. To counter this, we discuss a new model selection strategy that dramatically outperforms standard complexity-penalization and hold-out meth ods on these hard tasks. | [
848,
1053,
1223,
1335
] | Validation |
1,608 | 3 | Title: Combining estimates in regression and classification
Abstract: We consider the problem of how to combine a collection of general regression fit vectors in order to obtain a better predictive model. The individual fits may be from subset linear regression, ridge regression, or something more complex like a neural network. We develop a general framework for this problem and examine a recent cross-validation-based proposal called "stacking" in this context. Combination methods based on the bootstrap and analytic methods are also derived and compared in a number of examples, including best subsets regression and regression trees. Finally, we apply these ideas to classification problems where the estimated combination weights can yield insight into the structure of the problem. | [
431,
949,
987,
1220,
1512,
2225
] | Test |
1,609 | 5 | Title: Prognosing the femoral neck fracture recovery with machine learning
Abstract: We compare the performance and explanation abilities of several machine learning algorithms in the problem of predicting the femoral neck fracture recovery. Among different algorithms, the semi naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving the prediction problem and show that the combined classifier improves both the performance and explanation ability. | [
1569
] | Validation |
1,610 | 2 | Title: Using Fourier-Neural Recurrent Networks to Fit Sequential Input/Output Data
Abstract: This paper suggests the use of Fourier-type activation functions in fully recurrent neural networks. The main theoretical advantage is that, in principle, the problem of recovering internal coefficients from input/output data is solvable in closed form. | [
1028,
1037
] | Train |
1,611 | 1 | Title: Island Model Genetic Algorithms and Linearly Separable Problems
Abstract: Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model Genetic Algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. On the other hand, linearly separable functions have often been used to test Island Model Genetic Algorithms; it is possible that Island Models are particular well suited to separable problems. We look at how Island Models can track multiple search trajectories using the infinite population models of the simple genetic algorithm. We also introduce a simple model for better understanding when Island Model Genetic Algorithms may have an advantage when processing linearly separable problems. | [
100,
163,
1153,
1379,
1380
] | Train |
1,612 | 2 | Title: Bootstrapping with Noise: An Effective Regularization Technique
Abstract: Bootstrap samples with noise are shown to be an effective smoothness and capacity control technique for training feed-forward networks and for other statistical methods such as generalized additive models. It is shown that noisy bootstrap performs best in conjunction with weight decay regularization and ensemble averaging. The two-spiral problem, a highly non-linear noise-free data, is used to demonstrate these findings. The combination of noisy bootstrap and ensemble averaging is also shown useful for generalized additive modeling, and is also demonstrated on the well known Cleveland Heart Data [7]. | [
1517
] | Train |
1,613 | 3 | Title: Priors and Component Structures in Autoregressive Time Series Models
Abstract: New approaches to prior specification and structuring in autoregressive time series models are introduced and developed. We focus on defining classes of prior distributions for parameters and latent variables related to latent components of an autoregressive model for an observed time series. These new priors naturally permit the incorporation of both qualitative and quantitative prior information about the number and relative importance of physically meaningful components that represent low frequency trends, quasi-periodic sub-processes, and high frequency residual noise components of observed series. The class of priors also naturally incorporates uncertainty about model order, and hence leads in posterior analysis to model order assessment and resulting posterior and predictive inferences that incorporate full uncertainties about model order as well as model parameters. Analysis also formally incorporates uncertainty, and leads to inferences about, unknown initial values of the time series, as it does for predictions of future values. Posterior analysis involves easily implemented iterative simulation methods, developed and described here. One motivating applied field is climatology, where the evaluation of latent structure, especially quasi-periodic structure, is of critical importance in connection with issues of global climatic variability. We explore analysis of data from the Southern Oscillation Index (SOI), one of several series that has been central in recent high-profile debates in the atmospheric sciences about recent apparent trends in climatic indicators. | [
99,
784,
1162,
1614,
1619
] | Validation |
1,614 | 3 | Title: Bayesian Inference on Periodicities and Component Spectral Structure in Time Series
Abstract: Summary We detail and illustrate time series analysis and spectral inference in autoregressive models with a focus on the underlying latent structure and time series decompositions. A novel class of priors on parameters of latent components leads to a new class of smoothness priors on autoregressive coefficients, provides for formal inference on model order, including very high order models, and leads to the incorporation of uncertainty about model order into summary inferences. The class of prior models also allows for subsets of unit roots, and hence leads to inference on sustained though stochastically time-varying periodicities in time series. Applications to analysis of the frequency composition of time series, in both time and spectral domains, is illustrated in a study of a time series from astronomy. This analyses demonstrates the impact and utility of the new class of priors in addressing model order uncertainty and in allowing for unit root structure. Time domain decomposition of a time series into estimated latent components provides an important alternative view of the component spectral characteristics of a series. In addition, our data analysis illustrates the utility of the smoothness prior and allowance for unit root structure in inference about spectral densities. In particular, the framework overcomes supposed problems in spectral estimation with autoregressive models using more traditional model fitting methods. | [
1613,
1619
] | Validation |
1,615 | 2 | Title: A Provably Convergent Dynamic Training Method for Multilayer Perceptron Networks
Abstract: This paper presents a new method for training multilayer perceptron networks called DMP1 (Dynamic Multilayer Perceptron 1). The method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. The individual nodes of the network are trained using a gentetic algorithm. The method is capable of handling real-valued inputs and a proof is given concerning its convergence properties of the basic model. Simulation results show that DMP1 performs favorably in comparison with other learning algorithms. | [
809,
1229,
1639
] | Train |
1,616 | 4 | Title: NeuroDraughts: the role of representation, search, training regime and architecture in a TD draughts player
Abstract: NeuroDraughts is a draughts playing program similar in approach to NeuroGammon and NeuroChess [Tesauro, 1992, Thrun, 1995]. It uses an artificial neural network trained by the method of temporal difference learning to learn by self-play how to play the game of draughts. This paper discusses the relative contribution of board representation, search depth, training regime, architecture and run time parameters to the strength of the TDplayer produced by the system. Keywords: Temporal Difference Learning, Input representation, Search, Draughts. | [
523,
565,
882
] | Validation |
1,617 | 0 | Title: Knowledge Discovery in International Conflict Databases
Abstract: Artificial Intelligence is heavily supported by military institutions, while practically no effort goes into the investigation of possible contributions of AI to the avoidance and termination of crises and wars. This paper makes a first step into this direction by investigating the use of machine learning techniques for discovering knowledge in international conflict and conflict management databases. We have applied similarity-based case retrieval to the KOSIMO database of international conflicts. Furthermore, we present results of analyzing the CONFMAN database of successful and unsuccessful conflict management attempts with an inductive decision tree learning algorithm. The latter approach seems to be particularly promising, as conflict management events apparently are more repetitive and thus better suited for machine-aided analysis. | [
236,
430,
1107
] | Test |
1,618 | 6 | Title: Selection of Relevant Features and Examples in Machine Learning
Abstract: In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area. | [
1112,
1220,
2343
] | Validation |
1,619 | 3 | Title: Exploratory Modelling of Multiple Non-Stationary Time Series: Latent Process Structure and Decompositions
Abstract: We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-stationary time series. This begins with uni-variate models for collections of related time series assumedly driven by underlying but unobservable processes, referred to as dynamic latent factor processes. We focus on models in which the factor processes, and hence the observed time series, are modelled by time-varying autoregressions capable of flexibly representing ranges of observed non-stationary characteristics. We highlight concepts and new methods of time series decomposition to infer characteristics of latent components in time series, and relate uni-variate decomposition analyses to underlying multivariate dynamic factor structure. Our motivating application is in analysis of multiple EEG traces from an ongoing EEG study at Duke. In this study, individuals undergoing ECT therapy generate multiple EEG traces at various scalp locations, and physiological interest lies in identifying dependencies and dissimilarities across series. In addition to the multivariate and non-stationary aspects of the series, this area provides illustration of the new results about decomposition of time series into latent, physically interpretable components; this is illustrated in data analysis of one EEG data set. The paper also discusses current and future research directions. fl This research was supported in part by the National Science Foundation under grant DMS-9311071. The EEG data and context arose from discussions with Dr Andrew Krystal, of Duke University Medical Center, with whom continued interactions have been most valuable. Address for correspondence: Institute of Statistics and Decision Sciences, Duke University, Durham, NC 27708-0251 U.S.A. (http://www.stat.duke.edu) | [
99,
1613,
1614,
1722,
1723
] | Test |
1,620 | 5 | Title: Efficient -Subsumption based on Graph Algorithms
Abstract: The -subsumption problem is crucial to the efficiency of ILP learning systems. We discuss two -subsumption algorithms based on strategies for preselecting suitable matching literals. The class of clauses, for which subsumption becomes polynomial, is a superset of the deterministic clauses. We further map the general problem of -subsumption to a certain problem of finding a clique of fixed size in a graph, and in return show that a specialization of the pruning strategy of the Car-raghan and Pardalos clique algorithm provides a dramatic reduction of the subsumption search space. We also present empirical results for the mesh design data set. | [
1177,
1180
] | Train |
1,621 | 0 | Title: Evaluating the Effectiveness of Derivation Replay in Partial-order vs State-space Planning
Abstract: Case-based planning involves storing individual instances of problem-solving episodes and using them to tackle new planning problems. This paper is concerned with derivation replay, which is the main component of a form of case-based planning called derivational analogy (DA). Prior to this study, implementations of derivation replay have been based within state-space planning. We are motivated by the acknowledged superiority of partial-order (PO) planners in plan generation. Here we demonstrate that plan-space planning also has an advantage in replay. We will argue that the decoupling of planning (derivation) order and the execution order of plan steps, provided by partial-order planners, enables them to exploit the guidance of previous cases in a more efficient and straightforward fashion. We validate our hypothesis through a focused empirical comparison. | [
300,
594,
752,
824,
1194,
1448
] | Train |
1,622 | 5 | Title: Stochastic Propositionalization of Non-Determinate Background Knowledge
Abstract: It is a well-known fact that propositional learning algorithms require "good" features to perform well in practice. So a major step in data engineering for inductive learning is the construction of good features by domain experts. These features often represent properties of structured objects, where a property typically is the occurrence of a certain substructure having certain properties. To partly automate the process of "feature engineering", we devised an algorithm that searches for features which are defined by such substructures. The algorithm stochastically conducts a top-down search for first-order clauses, where each clause represents a binary feature. It differs from existing algorithms in that its search is not class-blind, and that it is capable of considering clauses ("context") of almost arbitrary length (size). Preliminary experiments are favorable, and support the view that this approach is promising. | [
344,
1312,
1322,
1428,
1578
] | Validation |
1,623 | 2 | Title: A Neural Network Model for the Gold Market
Abstract: A neural network trend predictor for the gold bullion market is presented. A simple recurrent neural network was trained to recognize turning points in the gold market based on a to-date history of ten market indices. The network was tested on data that was held back from training, and a significant amount of predictive power was observed. The turning point predictions can be used to time transactions in the gold bullion and gold mining company stock index markets to obtain a significant paper profit during the test period. The training data consisted of daily closing prices for the ten input markets for a period of about five years. The turning point targets were labeled for the training phase without the help of a financial expert. Thus, this experiment shows that useful predictions can be made without the use of more extensive market data or knowledge. | [
1313
] | Validation |
1,624 | 3 | Title: Causal Discovery via MML
Abstract: Automating the learning of causal models from sample data is a key step toward incorporating machine learning in the automation of decision-making and reasoning under uncertainty. This paper presents a Bayesian approach to the discovery of causal models, using a Minimum Message Length (MML) method. We have developed encoding and search methods for discovering linear causal models. The initial experimental results presented in this paper show that the MML induction approach can recover causal models from generated data which are quite accurate reflections of the original models; our results compare favorably with those of the TETRAD II program of Spirtes et al. [25] even when their algorithm is supplied with prior temporal information and MML is not. | [
1550
] | Validation |
1,625 | 4 | Title: Reinforcement Learning by Probability Matching
Abstract: We present a new algorithm for associative reinforcement learning. The algorithm is based upon the idea of matching a network's output probability with a probability distribution derived from the environment's reward signal. This Probability Matching algorithm is shown to perform faster and be less susceptible to local minima than previously existing algorithms. We use Probability Matching to train mixture of experts networks, an architecture for which other reinforcement learning rules fail to converge reliably on even simple problems. This architecture is particularly well suited for our algorithm as it can compute arbitrarily complex functions yet calculation of the output probability is simple. | [
1580
] | Train |
1,626 | 0 | Title: A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms
Abstract: Many lazy learning algorithms are derivatives of the k-nearest neighbor (k-NN) classifier, which uses a distance function to generate predictions from stored instances. Several studies have shown that k-NN's performance is highly sensitive to the definition of its distance function. Many k-NN variants have been proposed to reduce this sensitivity by parameterizing the distance function with feature weights. However, these variants have not been categorized nor empirically compared. This paper reviews a class of weight-setting methods for lazy learning algorithms. We introduce a framework for distinguishing these methods and empirically compare them. We observed four trends from our experiments and conducted further studies to highlight them. Our results suggest that methods which use performance feedback to assign weight settings demonstrated three advantages over other methods: they require less pre-processing, perform better in the presence of interacting features, and generally require less training data to learn good settings. We also found that continuous weighting methods tend to outperform feature selection algorithms for tasks where some features are useful but less important than others. | [
1164,
1407,
1584,
1698,
1735
] | Train |
1,627 | 5 | Title: Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples
Abstract: This paper presents a novel idea to the problem of learning concept descriptions from examples. Whereas most existing approaches rely on a large number of classified examples, the approach presented in the paper is aimed at being applicable when only a few examples are classified as positive (and negative) instances of a concept. The approach tries to take advantage of the information which can be induced from descriptions of unclassified objects using a conceptual clustering algorithm. The system Cola is described and results of applying Cola in two real-world domains are presented. | [
344,
479,
1177
] | Train |
1,628 | 1 | Title: Local Selection
Abstract: Local selection (LS) is a very simple selection scheme in evolutionary algorithms. Individual fitnesses are compared to a fixed threshold, rather than to each other, to decide who gets to reproduce. LS, coupled with fitness functions stemming from the consumption of shared environmental resources, maintains diversity in a way similar to fitness sharing; however it is generally more efficient than fitness sharing, and lends itself to parallel implementations for distributed tasks. While LS is not prone to premature convergence, it applies minimal selection pressure upon the population. LS is therefore more appropriate than other, stronger selection schemes only on certain problem classes. This papers characterizes one broad class of problems in which LS consistently out performs tournament selection. | [
1063,
1175
] | Train |
1,629 | 2 | Title: Regional Stability of an ERS/JERS-1 Classifer
Abstract: The potential of combined ERS/JERS-1 SAR images for land cover classification was demonstrated for the Raco test site (Michigan) in recent papers and articles. Our goal is to develop a classification algorithm which is stable in terms of applicability in different geographical regions. Unlike optical remote sensing techniques, radar remote sensing can provide calibrated data where the image signal is solely determined by the physical (structural) and electrical properties of the targets on the Earth's surface and near subsurface. Hence, a classifier based on radar signatures of object classes should be applicable on new calibrated images without the need to train the classifier again. This article discusses the design and applicability of a classification algorithm, which is based on calibrated radar signatures measured from ERS-1 (C-band, vv polarized) and JERS-1 (L-band, hh polarized) SAR image data. The applicability is compared in two different test sites, Raco, Michigan and the Cedar Creek LTER site, Minnesota. It was found, that classes separate very well, when certain boundary conditions like comparable seasonality or soil moisture conditions are observed. | [
796
] | Validation |
1,630 | 2 | Title: Averaging and Data Snooping
Abstract: Presenting and Analyzing the Results of AI Experiments: Data Averaging and Data Snooping, Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI-97, AAAI Press, Menlo Park, California, pp. 362367, 1997. Copyright AAAI. Presenting and Analyzing the Results of AI Experiments: Abstract Experimental results reported in the machine learning AI literature can be misleading. This paper investigates the common processes of data averaging (reporting results in terms of the mean and standard deviation of the results from multiple trials) and data snooping in the context of neural networks, one of the most popular AI machine learning models. Both of these processes can result in misleading results and inaccurate conclusions. We demonstrate how easily this can happen and propose techniques for avoiding these very important problems. For data averaging, common presentation assumes that the distribution of individual results is Gaussian. However, we investigate the distribution for common problems and find that it often does not approximate the Gaussian distribution, may not be symmetric, and may be multimodal. We show that assuming Gaussian distributions can significantly affect the interpretation of results, especially those of comparison studies. For a controlled task, we find that the distribution of performance is skewed towards better performance for smoother target functions and skewed towards worse performance for more complex target functions. We propose new guidelines for reporting performance which provide more information about the actual distribution (e.g. box-whiskers plots). For data snooping, we demonstrate that optimization of performance via experimentation with multiple parameters can lead to significance being assigned to results which are due to chance. We suggest that precise descriptions of experimental techniques can be very important to the evaluation of results, and that we need to be aware of potential data snooping biases when formulating these experimental techniques (e.g. selecting the test procedure). Additionally, it is important to only rely on appropriate statistical tests and to ensure that any assumptions made in the tests are valid (e.g. normality of the distribution). | [
1150,
1195,
1203
] | Validation |
1,631 | 1 | Title: A Survey of Intron Research in Genetics
Abstract: A brief survey of biological research on non-coding DNA is presented here. There has been growing interest in the effects of non-coding segments in evolutionary algorithms (EAs). To better understand and conduct research on non-coding segments and EAs, it is important to understand the biological background of such work. This paper begins with a review of basic genetics and terminology, describes the different types of non-coding DNA, and then surveys recent intron research. | [
934,
2330,
2407,
2598,
2604
] | Validation |
1,632 | 4 | Title: Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study
Abstract: We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy but may behave differently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare two learning algorithms: TD-Q learning with linear neural networks (TD-Q) and Probabilistic Incremental Program Evolution (PIPE). TD-Q is based on evaluation functions (EFs) mapping input/action pairs to expected reward, while PIPE searches policy space directly. PIPE uses adaptive "probabilistic prototype trees" to synthesize programs that calculate action probabilities from current inputs. Our results show that TD-Q encounters several difficulties in learning appropriate shared EFs. PIPE, however, does not depend on EFs and can find good policies faster and more reliably. This suggests that in multiagent learning scenarios direct search through policy space can offer advantages over EF-based approaches. | [
68,
471,
1687
] | Train |
1,633 | 2 | Title: Changing Supply Functions in Input/State Stable Systems
Abstract: We consider the problem of characterizing possible supply functions for a given dissipative nonlinear system, and provide a result that allows some freedom in the modification of such functions. | [
1471
] | Train |
1,634 | 2 | Title: Combining Linear Discriminant Functions with Neural Networks for Supervised Learning
Abstract: A novel supervised learning method is presented by combining linear discriminant functions with neural networks. The proposed method results in a tree-structured hybrid architecture. Due to constructive learning, the binary tree hierarchical architecture is automatically generated by a controlled growing process for a specific supervised learning task. Unlike the classic decision tree, the linear discriminant functions are merely employed in the intermediate level of the tree for heuristically partitioning a large and complicated task into several smaller and simpler subtasks in the proposed method. These subtasks are dealt with by component neural networks at the leaves of the tree accordingly. For constructive learning, growing and credit-assignment algorithms are developed to serve for the hybrid architecture. The proposed architecture provides an efficient way to apply existing neural networks (e.g. multi-layered perceptron) for solving a large scale problem. We have already applied the proposed method to a universal approximation problem and several benchmark classification problems in order to evaluate its performance. Simulation results have shown that the proposed method yields better results and faster training in comparison with the multi-layered perceptron. | [
74,
1252
] | Test |
1,635 | 0 | Title: Redesigning control knowledge of knowledge-based systems: machine learning meets knowledge engineering
Abstract: Machine learning and knowledge engineering have always been strongly related, but the introduction of new representations in knowledge engineering has created a gap between them. This paper describes research aimed at applying machine learning techniques to the current knowledge engineering representations. We propose a system that redesigns a part of a knowledge based system, the so called control knowledge. We claim a strong similarity between redesign of knowledge based systems and incremental machine learning. Finally we will relate this work to existing research. | [
1214,
1706
] | Train |
1,636 | 0 | Title: Context-Sensitive Feature Selection for Lazy Learners
Abstract: | [
245,
928,
983,
1073,
1684,
2074
] | Validation |
1,637 | 2 | Title: The Effective Size of a Neural Network: A Principal Component Approach
Abstract: Often when learning from data, one attaches a penalty term to a standard error term in an attempt to prefer simple models and prevent overfitting. Current penalty terms for neural networks, however, often do not take into account weight interaction. This is a critical drawback since the effective number of parameters in a network usually differs dramatically from the total number of possible parameters. In this paper, we present a penalty term that uses Principal Component Analysis to help detect functional redundancy in a neural network. Results show that our new algorithm gives a much more accurate estimate of network complexity than do standard approaches. As a result, our new term should be able to improve techniques that make use of a penalty term, such as weight decay, weight pruning, feature selection, Bayesian, and prediction-risk tech niques. | [
157,
430,
1562
] | Validation |
1,638 | 1 | Title: Walsh Functions and Predicting Problem Complexity
Abstract: | [
1441
] | Validation |
1,639 | 2 | Title: The Effect of Decision Surface Fitness on Dynamic Multilayer Perceptron Networks (DMP1)
Abstract: The DMP1 (Dynamic Multilayer Perceptron 1) network training method is based upon a divide and conquer approach which builds networks in the form of binary trees, dynamically allocating nodes and layers as needed. This paper introduces the DMP1 method, and compares the preformance of DMP1 when using the standard delta rule training method for training individual nodes against the performance of DMP1 when using a genetic algorithm for training. While the basic model does not require the use of a genetic algorithm for training individual nodes, the results show that the convergence properties of DMP1 are enhanced by the use of a genetic algorithm with an appropriate fitness function. | [
809,
1615
] | Train |
1,640 | 0 | Title: KRITIK: AN EARLY CASE-BASED DESIGN SYSTEM
Abstract: In the late 1980s, we developed one of the early case-based design systems called Kritik. Kritik autonomously generated preliminary (conceptual, qualitative) designs for physical devices by retrieving and adapting past designs stored in its case memory. Each case in the system had an associated structure-behavior-function (SBF) device model that explained how the structure of the device accomplished its functions. These casespecific device models guided the process of modifying a past design to meet the functional specification of a new design problem. The device models also enabled verification of the design modifications. Kritik2 is a new and more complete implementation of Kritik. In this paper, we take a retrospective view on Kritik. In early papers, we had described Kritik as integrating case-based and model-based reasoning. In this integration, Kritik also grounds the computational process of case-based reasoning in the SBF content theory of device comprehension. The SBF models not only provide methods for many specific tasks in case-based design such as design adaptation and verification, but they also provide the vocabulary for the whole process of case-based design, from retrieval of old cases to storage of new ones. This grounding, we believe, is essential for building well-constrained theories of case-based design. | [
540,
603,
1121,
1345,
1355,
2706
] | Test |
1,641 | 3 | Title: Learning Bayesian Networks from Incomplete Data
Abstract: Much of the current research in learning Bayesian Networks fails to effectively deal with missing data. Most of the methods assume that the data is complete, or make the data complete using fairly ad-hoc methods; other methods do deal with missing data but learn only the conditional probabilities, assuming that the structure is known. We present a principled approach to learn both the Bayesian network structure as well as the conditional probabilities from incomplete data. The proposed algorithm is an iterative method that uses a combination of Expectation-Maximization (EM) and Imputation techniques. Results are presented on synthetic data sets which show that the performance of the new algorithm is much better than ad-hoc methods for handling missing data. | [
71,
558,
1086
] | Test |
1,642 | 0 | Title: CHIRON: Planning in an Open-Textured Domain
Abstract: Much of the current research in learning Bayesian Networks fails to effectively deal with missing data. Most of the methods assume that the data is complete, or make the data complete using fairly ad-hoc methods; other methods do deal with missing data but learn only the conditional probabilities, assuming that the structure is known. We present a principled approach to learn both the Bayesian network structure as well as the conditional probabilities from incomplete data. The proposed algorithm is an iterative method that uses a combination of Expectation-Maximization (EM) and Imputation techniques. Results are presented on synthetic data sets which show that the performance of the new algorithm is much better than ad-hoc methods for handling missing data. | [
313,
986,
1377,
1475
] | Train |
1,643 | 4 | Title: Learning to coordinate without sharing information
Abstract: Researchers in the field of Distributed Artificial Intelligence (DAI) have been developing efficient mechanisms to coordinate the activities of multiple autonomous agents. The need for coordination arises because agents have to share resources and expertise required to achieve their goals. Previous work in the area includes using sophisticated information exchange protocols, investigating heuristics for negotiation, and developing formal models of possibilities of conflict and cooperation among agent interests. In order to handle the changing requirements of continuous and dynamic environments, we propose learning as a means to provide additional possibilities for effective coordination. We use reinforcement learning techniques on a block pushing problem to show that agents can learn complimentary policies to follow a desired path without any knowledge about each other. We theoretically analyze and experimentally verify the effects of learning rate on system convergence, and demonstrate benefits of using learned coordination knowledge on similar problems. Reinforcement learning based coordination can be achieved in both cooperative and non-cooperative domains, and in domains with noisy communication channels and other stochastic characteristics that present a formidable challenge to using other coordination schemes. | [
566,
649,
773,
868,
1189,
1649
] | Train |
1,644 | 2 | Title: A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping
Abstract: The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially. A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping. | [
318,
322,
378,
462,
701,
822,
986,
1256,
1290,
1328,
1601,
1732,
1862,
1863,
1964,
2364,
2409,
2423,
2484,
2614,
2616
] | Train |
1,645 | 2 | Title: Acquiring the mapping from meaning to sounds
Abstract: 1 We thank Steen Ladegaard Knudsen for his assistance in programming, analysis and running of simulations, Scott Baden for his assistance in vectorizing our code for the Cray Y-MP, the Division of Engineering Block Grant for time on the Cray at the San Diego Supercomputer Center, and the members of the PDPNLP and GURU Research Groups at UCSD for helpful comments on earlier versions of this work. | [
204,
477,
797
] | Test |
1,646 | 1 | Title: Generation Gaps Revisited
Abstract: There has been a lot of recent interest in so-called "steady state" genetic algorithms (GAs) which, among other things, replace only a few individuals (typically 1 or 2) each generation from a fixed size population of size N. Understanding the advantages and/or disadvantages of replacing only a fraction of the population each generation (rather than the entire population) was a goal of some of the earliest GA research. In spite of considerable progress in our understanding of GAs since then, the pros/cons of overlapping generations remains a somewhat cloudy issue. However, recent theoretical and empirical results provide the background for a much clearer understanding of this issue. In this paper we review, combine, and extend these results in a way that significantly sharpens our insight. | [
145,
943
] | Train |
1,647 | 6 | Title: Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)
Abstract: Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause more or less radical changes in the concepts. Incremental concept learning in such domains requires the ability to recognize and adapt to such changes. This paper presents a solution for incremental learning tasks where the domain provides explicit clues as to the current context (e.g., attributes with characteristic values). We present a general two-level learning model, and its realization in a system named MetaL(B), that can learn to detect certain types of contextual clues, and can react accordingly when a context change is suspected. The model consists of a base level learner that performs the regular on-line learning and classification task, and a meta-learner that identifies potential contextual clues. Context learning and detection occur during regular on-line learning, without separate training phases for context recognition. Experiments with synthetic domains as well as a `real-world' problem show that MetaL(B) is robust in a variety of dimensions and produces substantial improvement over simple object-level learning in situations with changing contexts. | [
1684,
1908,
2074,
2586,
2615
] | Train |
1,648 | 0 | Title: Creative Design: Reasoning and Understanding
Abstract: This paper investigates memory issues that influence long- term creative problem solving and design activity, taking a case-based reasoning perspective. Our exploration is based on a well-documented example: the invention of the telephone by Alexander Graham Bell. We abstract Bell's reasoning and understanding mechanisms that appear time and again in long-term creative design. We identify that the understanding mechanism is responsible for analogical anticipation of design constraints and analogical evaluation, beside case-based design. But an already understood design can satisfy opportunistically suspended design problems, still active in background. The new mechanisms are integrated in a computational model, ALEC 1 , that accounts for some creative be | [
1355
] | Train |
1,649 | 4 | Title: Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents
Abstract: Intelligent human agents exist in a cooperative social environment that facilitates learning. They learn not only by trial-and-error, but also through cooperation by sharing instantaneous information, episodic experience, and learned knowledge. The key investigations of this paper are, "Given the same number of reinforcement learning agents, will cooperative agents outperform independent agents who do not communicate during learning?" and "What is the price for such cooperation?" Using independent agents as a benchmark, cooperative agents are studied in following ways: (1) sharing sensation, (2) sharing episodes, and (3) sharing learned policies. This paper shows that (a) additional sensation from another agent is beneficial if it can be used efficiently, (b) sharing learned policies or episodes among agents speeds up learning at the cost of communication, and (c) for joint tasks, agents engaging in partnership can significantly outperform independent agents although they may learn slowly in the beginning. These tradeoffs are not just limited to multi-agent reinforcement learning. | [
148,
691,
868,
1213,
1228,
1557,
1643,
1687
] | Train |
1,650 | 1 | Title: Genetic Algorithms For Vertex Splitting in DAGs 1
Abstract: 1 This paper has been submitted to the 5th International Conference on Genetic Algorithms 2 electronic mail address: matze@cs.umr.edu 3 electronic mail address: ercal@cs.umr.edu | [
163,
1466
] | Train |
1,651 | 5 | Title: An application of ILP in a musical database: learning to compose the two-voice counterpoint
Abstract: We describe SFOIL, a descendant of FOIL that uses the advanced stochastic search heuristic, and its application in learning to compose the two-voice counterpoint. The application required learning a 4-ary relation from more than 20.000 training instances. SFOIL is able to efficiently deal with this learning task which is to our knowledge one of the most complex learning task solved by an ILP system. This demonstrates that ILP systems can scale up to real databases and that top-down ILP systems that use the covering approach and advanced search strategies are appropriate for knowledge discovery in databases and are promising for further investigation. | [
877,
1010,
1061,
1578
] | Train |
1,652 | 2 | Title: A Theory of Visual Relative Motion Perception: Grouping, Binding, and Gestalt Organization
Abstract: The human visual system is more sensitive to the relative motion of objects than to their absolute motion. An understanding of motion perception requires an understanding of how neural circuits can group moving visual elements relative to one another, based upon hierarchical reference frames. We have modeled visual relative motion perception using a neural network architecture that groups visual elements according to Gestalt common-fate principles and exploits information about the behavior of each group to predict the behavior of individual elements. A simple competitive neural circuit binds visual elements together into a representation of a visual object. Information about the spiking pattern of neurons allows transfer of the bindings of an object representation from location to location in the neural circuit as the object moves. The model exhibits characteristics of human object grouping and solves some key neural circuit design problems in visual relative motion perception. | [
1659
] | Train |
1,653 | 0 | Title: Problem Solving for Redesign
Abstract: A knowledge-level analysis of complex tasks like diagnosis and design can give us a better understanding of these tasks in terms of the goals they aim to achieve and the different ways to achieve these goals. In this paper we present a knowledge-level analysis of redesign. Redesign is viewed as a family of methods based on some common principles, and a number of dimensions along which redesign problem solving methods can vary are distinguished. By examining the problem-solving behavior of a number of existing redesign systems and approaches, we came up with a collection of problem-solving methods for redesign and developed a task-method structure for redesign. In constructing a system for redesign a large number of knowledge-related choices and decisions are made. In order to describe all relevant choices in redesign problem solving, we have to extend the current notion of possible relations between tasks and methods in a PSM architecture. The realization of a task by a problem-solving method, and the decomposition of a problem-solving method into subtasks are the most common relations in a PSM architecture. However, we suggest to extend these relations with the notions of task refinement and method refinement. These notions represent intermediate decisions in a task-method structure, in which the competence of a task or method is refined without immediately paying attention to its operationalization in terms of subtasks. Explicit representation of this kind of intermediate decisions helps to make and represent decisions in a more piecemeal fashion. | [
1385
] | Test |
1,654 | 3 | Title: Hyperparameter estimation in Dirichlet process mixture models
Abstract: In Bayesian density estimation and prediction using Dirichlet process mixtures of standard, exponential family distributions, the precision or total mass parameter of the mixing Dirichlet process is a critical hyperparame-ter that strongly influences resulting inferences about numbers of mixture components. This note shows how, with respect to a flexible class of prior distributions for this parameter, the posterior may be represented in a simple conditional form that is easily simulated. As a result, inference about this key quantity may be developed in tandem with the existing, routine Gibbs sampling algorithms for fitting such mixture models. The concept of data augmentation is important, as ever, in developing this extension of the existing algorithm. A final section notes an simple asymptotic approx imation to the posterior. | [
852,
855,
1338
] | Train |
1,655 | 2 | Title: Strategies for the Parallel Training of Simple Recurrent Neural Networks
Abstract: In Bayesian density estimation and prediction using Dirichlet process mixtures of standard, exponential family distributions, the precision or total mass parameter of the mixing Dirichlet process is a critical hyperparame-ter that strongly influences resulting inferences about numbers of mixture components. This note shows how, with respect to a flexible class of prior distributions for this parameter, the posterior may be represented in a simple conditional form that is easily simulated. As a result, inference about this key quantity may be developed in tandem with the existing, routine Gibbs sampling algorithms for fitting such mixture models. The concept of data augmentation is important, as ever, in developing this extension of the existing algorithm. A final section notes an simple asymptotic approx imation to the posterior. | [
1313
] | Train |
1,656 | 2 | Title: Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification
Abstract: Technical report CNS-TR-95-1 Center for Neural Systems McMaster University | [
527,
731,
1710,
1822,
2357
] | Train |
1,657 | 2 | Title: Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain
Abstract: In this paper we describe our study of applying knowledge-based neural networks to the problem of diagnosing faults in local telephone loops. Currently, NYNEX uses an expert system called MAX to aid human experts in diagnosing these faults; however, having an effective learning algorithm in place of MAX would allow easy portability between different maintenance centers, and easy updating when the phone equipment changes. We find that (i) machine learning algorithms have better accuracy than MAX, (ii) neural networks perform better than decision trees, (iii) neural network ensembles perform better than standard neural networks, (iv) knowledge-based neural networks perform better than standard neural networks, and (v) an ensemble of knowledge-based neural networks performs the best. | [
1307,
1422,
1462,
1595
] | Train |
1,658 | 6 | Title: Learning One-Dimensional Geometric Patterns Under One-Sided Random Misclassification Noise
Abstract: In this paper we describe our study of applying knowledge-based neural networks to the problem of diagnosing faults in local telephone loops. Currently, NYNEX uses an expert system called MAX to aid human experts in diagnosing these faults; however, having an effective learning algorithm in place of MAX would allow easy portability between different maintenance centers, and easy updating when the phone equipment changes. We find that (i) machine learning algorithms have better accuracy than MAX, (ii) neural networks perform better than decision trees, (iii) neural network ensembles perform better than standard neural networks, (iv) knowledge-based neural networks perform better than standard neural networks, and (v) an ensemble of knowledge-based neural networks performs the best. | [
884
] | Validation |
1,659 | 2 | Title: In Unsmearing Visual Motion: Development of Long-Range Horizontal Intrinsic Connections
Abstract: Human vision systems integrate information nonlocally, across long spatial ranges. For example, a moving stimulus appears smeared when viewed briefly (30 ms), yet sharp when viewed for a longer exposure (100 ms) (Burr, 1980). This suggests that visual systems combine information along a trajectory that matches the motion of the stimulus. Our self-organizing neural network model shows how developmental exposure to moving stimuli can direct the formation of horizontal trajectory-specific motion integration pathways that unsmear representations of moving stimuli. These results account for Burr's data and can potentially also model other phenomena, such as visual inertia. | [
1093,
1094,
1652
] | Validation |
1,660 | 3 | Title: Explaining "Explaining Away"
Abstract: Explaining away is a common pattern of reasoning in which the confirmation of one cause of an observed or believed event reduces the need to invoke alternative causes. The opposite of explaining away also can occur, in which the confirmation of one cause increases belief in another. We provide a general qualitative probabilistic analysis of intercausal reasoning, and identify the property of the interaction among the causes, product synergy, that determines which form of reasoning is appropriate. Product synergy extends the qualitative probabilistic network (QPN) formalism to support qualitative intercausal inference about the directions of change in probabilistic belief. The intercausal relation also justifies Occam's razor, facilitating pruning in search for likely diagnoses. 0 Portions of this paper originally appeared in Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning [16]. y Supported by the National Science Foundation under grant IRI-8807061 to Carnegie Mellon and by the Rockwell International Science Center. | [
952,
1083
] | Test |
1,661 | 6 | Title: Simulating Access to Hidden Information while Learning
Abstract: We introduce a new technique which enables a learner without access to hidden information to learn nearly as well as a learner with access to hidden information. We apply our technique to solve an open problem of Maass and Turan [18], showing that for any concept class F , the least number of queries sufficient for learning F by an algorithm which has access only to arbitrary equivalence queries is at most a factor of 1= log 2 (4=3) more than the least number of queries sufficient for learning F by an algorithm which has access to both arbitrary equivalence queries and membership queries. Previously known results imply that the 1= log 2 (4=3) in our bound is best possible. We describe analogous results for two generalizations of this model to function learning, and apply those results to bound the difficulty of learning in the harder of these models in terms of the difficulty of learning in the easier model. We bound the difficulty of learning unions of k concepts from a class F in terms of the difficulty of learning F . We bound the difficulty of learning in a noisy environment for deterministic algorithms in terms of the difficulty of learning in a noise-free environment. We apply a variant of our technique to develop an algorithm transformation that allows probabilistic learning algorithms to nearly optimally cope with noise. A second variant enables us to improve a general lower bound of Turan [19] for the PAC-learning model (with queries). Finally, we show that logarithmically many membership queries never help to obtain computationally efficient learning algorithms. fl Supported by Air Force Office of Scientific Research grant F49620-92-J-0515. Most of this work was done while this author was at TU Graz supported by a Lise Meitner Fellowship from the Fonds zur Forderung der wissenschaftlichen Forschung (Austria). | [
109,
453,
791,
1358,
1469
] | Train |
1,662 | 1 | Title: Evolution of Homing Navigation in a Real Mobile Robot
Abstract: In this paper we describe the evolution of a discrete-time recurrent neural network to control a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely on the physical robot without human intervention. We show that the autonomous development of a set of behaviors for locating a battery charger and periodically returning to it can be achieved by lifting constraints in the design of the robot/environment interactions that were employed in a preliminary experiment. The emergent homing behavior is based on the autonomous development of an internal neural topographic map (which is not pre-designed) that allows the robot to choose the appropriate trajectory as function of location and remaining energy. | [
1036
] | Train |
1,663 | 1 | Title: Evolution of the Topology and the Weights of Neural Networks using Genetic Programming with a
Abstract: Genetic programming is a methodology for program development, consisting of a special form of genetic algorithm capable of handling parse trees representing programs, that has been successfully applied to a variety of problems. In this paper a new approach to the construction of neural networks based on genetic programming is presented. A linear chromosome is combined to a graph representation of the network and new operators are introduced, which allow the evolution of the architecture and the weights simultaneously without the need of local weight optimization. This paper describes the approach, the operators and reports results of the application of the model to several binary classification problems. | [
1266,
1756,
2504
] | Train |
1,664 | 2 | Title: "What is the best thing to do right now?": getting beyond greedy exploration
Abstract: Genetic programming is a methodology for program development, consisting of a special form of genetic algorithm capable of handling parse trees representing programs, that has been successfully applied to a variety of problems. In this paper a new approach to the construction of neural networks based on genetic programming is presented. A linear chromosome is combined to a graph representation of the network and new operators are introduced, which allow the evolution of the architecture and the weights simultaneously without the need of local weight optimization. This paper describes the approach, the operators and reports results of the application of the model to several binary classification problems. | [
740,
804,
1559,
1697
] | Train |
1,665 | 0 | Title: Conceptual Analogy: Conceptual clustering for informed and efficient analogical reasoning
Abstract: Conceptual analogy (CA) is a general approach that applies conceptual clustering and concept representations to facilitate the efficient use of past experiences (cases) during analogical reasoning (Borner 1995). The approach was developed and implemented in SYN* (see also (Borner 1994, Borner and Faauer 1995)) to support the design of supply nets in building engineering. This paper sketches the task; it outlines the nearest-neighbor-based agglomerative conceptual clustering applied in organizing large amounts of structured cases into case classes; it provides the concept representation used to characterize case classes and shows the analogous solution of new problems based on the concepts available. However, the main purpose of this paper is to evaluate CA in terms of its reasoning efficiency; its capability to derive solutions that go beyond the cases in the case base but still preserve the quality of cases. | [
539,
883
] | Train |
1,666 | 3 | Title: Efficient Non-parametric Estimation of Probability Density Functions
Abstract: Accurate and fast estimation of probability density functions is crucial for satisfactory computational performance in many scientific problems. When the type of density is known a priori, then the problem becomes statistical estimation of parameters from the observed values. In the non-parametric case, usual estimators make use of kernel functions. If X j ; j = 1; 2; : : : ; n is a sequence of i.i.d. random variables with estimated probability density function f n , in the kernel method the computation of the values f n (X 1 ); f n (X 2 ); : : : ; f n (X n ) requires O(n 2 ) operations, since each kernel needs to be evaluated at every X j . We propose a sequence of special weight functions for the non-parametric estimation of f which requires almost linear time: if m is a slowly growing function that increases without bound with n, our method requires only O(m 2 n) arithmetic operations. We derive conditions for convergence under a number of metrics, which turn out to be similar to those required for the convergence of kernel based methods. We also discuss experiments on different distributions and compare the efficiency and the accuracy of our computations with kernel based estimators for various values of n and m. | [
719,
1133
] | Train |
1,667 | 2 | Title: Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons
Abstract: We propose an active learning method with hidden-unit reduction, which is devised specially for multilayer perceptrons (MLP). First, we review our active learning method, and point out that many Fisher-information-based methods applied to MLP have a critical problem: the information matrix may be singular. To solve this problem, we derive the singularity condition of an information matrix, and propose an active learning technique that is applicable to | [
740,
1697
] | Test |
1,668 | 2 | Title: Space-Frequency Localized Basis Function Networks for Nonlinear System Estimation and Control
Abstract: Stable neural network control and estimation may be viewed formally as a merging of concepts from nonlinear dynamic systems theory with tools from multivariate approximation theory. This paper extends earlier results on adaptive control and estimation of nonlinear systems using gaussian radial basis functions to the on-line generation of irregularly sampled networks, using tools from multiresolution analysis and wavelet theory. This yields much more compact and efficient system representations while preserving global closed-loop stability. Approximation models employing basis functions that are localized in both space and spatial frequency admit a measure of the approximated function's spatial frequency content that is not directly dependent on reconstruction error. As a result, these models afford a means of adaptively selecting basis functions according to the local spatial frequency content of the approximated function. An algorithm for stable, on-line adaptation of output weights simultaneously with node configuration in a class of non-parametric models with wavelet basis functions is presented. An asymptotic bound on the error in the network's reconstruction is derived and shown to be dependent solely on the minimum approximation error associated with the steady state node configuration. In addition, prior bounds on the temporal bandwidth of the system to be identified or controlled are used to develop a criterion for on-line selection of radial and ridge wavelet basis functions, thus reducing the rate of increase in network's size with the dimension of the state vector. Experimental results obtained by using the network to predict the path of an unknown light bluff object thrown through air, in an active-vision based robotic catching system, are given to illustrate the network's performance in a simple real-time application. | [
611,
1488,
1910,
2378,
2535
] | Validation |
1,669 | 6 | Title: Bias and the Quantification of Stability Bias and the Quantification of Stability Bias and the
Abstract: Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias. | [
1236
] | Train |
1,670 | 1 | Title: Raising GA Performance by Simultaneous Tuning of Selective Pressure and Recombination Disruptiveness
Abstract: In many Genetic Algorithms applications the objective is to find a (near-)optimal solution using a limited amount of computation. Given these requirements it is difficult to find a good balance between exploration and exploitation. Usually such a balance is found by tuning the various parameters (like the selective pressure, population size, the mutation- and crossover rate) of the Genetic Algorithm. As an alternative we propose simultaneous tuning of the selective pressure and the disruptiveness of the recombination operators. Our experiments show that the combination of a proper selective pressure and a highly disruptive recombination operator yields superior performance. The reduction mechanism used in a Steady-State GA has a strong influence on the optimal crossover disruptiveness. Using the worst fitness deletion strategy the building blocks present in the current best individuals are always preserved. This releases the crossover operator from the burden to maintain good building blocks and allows us to tune crossover disruptiveness to improve the search for better individuals. | [
1016,
1218
] | Validation |
1,671 | 6 | Title: From: Computational Learning Theory and Natural Systems, Chapter 18, "Cross-validation and Modal Theories", Cross-Validation and
Abstract: Cross-validation is a frequently used, intuitively pleasing technique for estimating the accuracy of theories learned by machine learning algorithms. During testing of a machine learning algorithm (foil) on new databases of prokaryotic RNA transcription promoters which we have developed, cross-validation displayed an interesting phenomenon. One theory is found repeatedly and is responsible for very little of the cross-validation error, whereas other theories are found very infrequently which tend to be responsible for the majority of the cross-validation error. It is tempting to believe that the most frequently found theory (the "modal theory") may be more accurate as a classifier of unseen data than the other theories. However, experiments showed that modal theories are not more accurate on unseen data than the other theories found less frequently during cross-validation. Modal theories may be useful in predicting when cross-validation is a poor estimate of true accuracy. We offer explanations 1 For correspondence: Department of Computer Science and Engineering, University of California, San | [
344,
1512
] | Test |
1,672 | 2 | Title: Learning Controllers for Industrial Robots
Abstract: One of the most significant cost factors in robotics applications is the design and development of real-time robot control software. Control theory helps when linear controllers have to be developed, but it doesn't sufficiently support the generation of non-linear controllers, although in many cases (such as in compliance control), nonlinear control is essential for achieving high performance. This paper discusses how Machine Learning has been applied to the design of (non-)linear controllers. Several alternative function approximators, including Multilayer Perceptrons (MLP), Radial Basis Function Networks (RBFNs), and Fuzzy Controllers are analyzed and compared, leading to the definition of two major families: Open Field Function Function Approximators and Locally Receptive Field Function Approximators. It is shown that RBFNs and Fuzzy Controllers bear strong similarities, and that both have a symbolic interpretation. This characteristics allows for applying both symbolic and statistic learning algorithms to synthesize the network layout from a set of examples and, possibly, some background knowledge. Three integrated learning algorithms, two of which are original, are described and evaluated on experimental test cases. The first test case is provided by a robot KUKA IR-361 engaged into the "peg-into-hole" task, whereas the second is represented by a classical prediction task on the Mackey-Glass time series. From the experimental comparison, it appears that both Fuzzy Controllers and RBFNs synthesised from examples are excellent approximators, and that, in practice, they can be even more accurate than MLPs. | [
294,
696,
807,
899,
903,
1352,
1564
] | Test |
1,673 | 1 | Title: EVOLVING ROBOT BEHAVIORS
Abstract: This paper discusses the use of evolutionary computation to evolve behaviors that exhibit emergent intelligent behavior. Genetic algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the actual robot. Some of the emergent behavior is described in detail. | [
910,
964,
965,
966,
1573
] | Train |
1,674 | 0 | Title: Proceedings of CogSci89 Structural Evaluation of Analogies: What Counts?
Abstract: Judgments of similarity and soundness are important aspects of human analogical processing. This paper explores how these judgments can be modeled using SME, a simulation of Gentner's structure-mapping theory. We focus on structural evaluation, explicating several principles which psychologically plausible algorithms should follow. We introduce the Specificity Conjecture, which claims that naturalistic representations include a preponderance of appearance and low-order information. We demonstrate via computational experiments that this conjecture affects how structural evaluation should be performed, including the choice of normalization technique and how the systematicity preference is implemented. | [
1123,
1354,
1680
] | Train |
1,675 | 1 | Title: A Study of Genetic Algorithms to Find Approximate Solutions to Hard 3CNF Problems
Abstract: Genetic algorithms have been used to solve hard optimization problems ranging from the Travelling Salesman problem to the Quadratic Assignment problem. We show that the Simple Genetic Algorithm can be used to solve an optimization problem derived from the 3-Conjunctive Normal Form problem. By separating the populations into small sub-populations, parallel genetic algorithms exploits the inherent parallelism in genetic algorithms and prevents premature convergence. Genetic algorithms using hill-climbing conduct genetic search in the space of local optima, and hill-climbing can be less com-putationally expensive than genetic search. We examine the effectiveness of these techniques in improving the quality of solutions of 3CNF problems. | [
163,
1153
] | Train |
1,676 | 2 | Title: TD Learning of Game Evaluation Functions with Hierarchical Neural Architectures
Abstract: Genetic algorithms have been used to solve hard optimization problems ranging from the Travelling Salesman problem to the Quadratic Assignment problem. We show that the Simple Genetic Algorithm can be used to solve an optimization problem derived from the 3-Conjunctive Normal Form problem. By separating the populations into small sub-populations, parallel genetic algorithms exploits the inherent parallelism in genetic algorithms and prevents premature convergence. Genetic algorithms using hill-climbing conduct genetic search in the space of local optima, and hill-climbing can be less com-putationally expensive than genetic search. We examine the effectiveness of these techniques in improving the quality of solutions of 3CNF problems. | [
74,
207,
523,
565,
1146
] | Train |
1,677 | 2 | Title: ICSIM: An Object Oriented Simulation Environment for Structured Connectionist Nets. Class Project Report, Physics 250
Abstract: ICSIM is a simulator for structured connectionism under development at ICSI. Structured connectionism is characterized by the need for flexibility, efficiency and support for the design and reuse of modular substructure. We take the position that a fast object-oriented language like Sather [5] is an appropriate implementation medium to achieve these goals. The core of ICSIM consists of a hierarchy of classes that correspond to simulation entities. New connectionist models are realized by combining and specializing pre-existing classes. Whenever possible, auxillary functionality has been separated out into functional modules in order to keep the basic hierarchy as clean and simple as possible. | [
1120
] | Train |
1,678 | 6 | Title: Induction of One-Level Decision Trees
Abstract: In recent years, researchers have made considerable progress on the worst-case analysis of inductive learning tasks, but for theoretical results to have impact on practice, they must deal with the average case. In this paper we present an average-case analysis of a simple algorithm that induces one-level decision trees for concepts defined by a single relevant attribute. Given knowledge about the number of training instances, the number of irrelevant attributes, the amount of class and attribute noise, and the class and attribute distributions, we derive the expected classification accuracy over the entire instance space. We then examine the predictions of this analysis for different settings of these domain parameters, comparing them to exper imental results to check our reasoning. | [
378,
861,
1339,
1570
] | Validation |
1,679 | 5 | Title: Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,
Abstract: We compare the performance of several machine learning algorithms in the problem of prognos-tics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, backpropagation with weight elimination learning of the multilayered neural networks, the LFC (lookahead feature construction) algorithm, and the Assistant-I and Assistant-R algorithms for top down induction of decision trees using information gain and RELIEFF as search heuristics, respectively. We compare the prognostic accuracy and the explanation ability of different classifiers. Among the different algorithms the semi-naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving prediction problems and show that the combined classifier improves both performance and the explanation ability. | [
627,
1569,
1726
] | Train |
1,680 | 0 | Title: Making SME greedy and pragmatic
Abstract: The Structure-Mapping Engine (SME) has successfully modeled several aspects of human consistent interpretations of an analogy. While useful for theoretical explorations, this aspect of the algorithm is both psychologically implausible and computationally inefficient. (2) SME contains no mechanism for focusing on interpretations relevant to an analogizer's goals. This paper describes modifications to SME which overcome these flaws. We describe a greedy merge algorithm which efficiently computes an approximate "best" interpretation, and can generate alternate interpretations when necessary. We describe pragmatic marking, a technique which focuses the mapping to produce relevant, yet novel, inferences. We illustrate these techniques via example and evaluate their performance using empirical data and theoretical analysis. analogical processing. However, it has two significant drawbacks: (1) SME constructs all structurally | [
1123,
1354,
1674
] | Train |
1,681 | 3 | Title: Understanding WaveShrink: Variance and Bias Estimation
Abstract: Research Report | [
1682
] | Train |
1,682 | 3 | Title: WaveShrink: Shrinkage Functions and Thresholds
Abstract: Donoho and Johnstone's WaveShrink procedure has proven valuable for signal de-noising and non-parametric regression. WaveShrink is based on the principle of shrinking wavelet coefficients towards zero to remove noise. WaveShrink has very broad asymptotic near-optimality properties. In this paper, we introduce a new shrinkage scheme, semisoft, which generalizes hard and soft shrinkage. We study the properties of the shrinkage functions, and demonstrate that semisoft shrinkage offers advantages over both hard shrinkage (uniformly smaller risk and less sensitivity to small perturbations in the data) and soft shrinkage (smaller bias and overall L 2 risk). We also construct approximate pointwise confidence intervals for WaveShrink and address the problem of threshold selection. | [
1681
] | Train |
1,683 | 6 | Title: An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks.
Abstract: Macquarie University Technical Report No. 95-173C Department of Computing School of MPCE, Macquarie University, New South Wales, Australia | [
740,
1198,
1559,
1697
] | Train |
1,684 | 5 | Title: Context-sensitive attribute estimation in regression
Abstract: One of key issues in both discrete and continuous class prediction and in machine learning in general seems to be the problem of estimating the quality of attributes. Heuristic measures mostly assume independence of attributes so their use is non-optimal in domains with strong dependencies between attributes. For the same reason they are also mostly unable to recognize context dependent features. Relief and its extension Re-liefF are statistical methods capable of correctly estimating the quality of attributes in classification problems with strong dependencies between attributes. By exploiting local information provided by different contexts they provide a global view and recognize contextual attributes. After the analysis of ReliefF we have extended it to continuous class problems. Regressional ReliefF (RReliefF) and ReliefF provide a unified view on estimating attribute quality. The experiments show that RReliefF correctly estimates the quality of attributes, recognizes the contextual attributes and can be used for non myopic learning of the regression trees. | [
314,
1182,
1569,
1636,
1647,
1726
] | Test |
1,685 | 1 | Title: Optimization by Means of Genetic Algorithms
Abstract: Genetic Algorithms (GAs) are powerful heuristic search strategies based upon a simple model of organic evolution. The basic working scheme of GAs as developed by Holland [Hol75] is described within this paper in a formal way, and extensions based upon the second-level learning principle for strategy parameters as introduced in Evolution Strategies (ESs) are proposed. First experimental results concerning this extension of GAs are also reported. | [
163,
422,
1069,
1455
] | Validation |
1,686 | 5 | Title: Learning with Abduction
Abstract: We investigate how abduction and induction can be integrated into a common learning framework through the notion of Abductive Concept Learning (ACL). ACL is an extension of Inductive Logic Programming (ILP) to the case in which both the background and the target theory are abductive logic programs and where an abductive notion of entailment is used as the coverage relation. In this framework, it is then possible to learn with incomplete information about the examples by exploiting the hypothetical reasoning of abduction. The paper presents the basic framework of ACL with its main characteristics and illustrates its potential in addressing several problems in ILP such as learning with incomplete information and multiple predicate learning. An algorithm for ACL is developed by suitably extending the top-down ILP method for concept learning and integrating this with an abductive proof procedure for Abductive Logic Programming (ALP). A prototype system has been developed and applied to learning problems with incomplete information. The particular role of integrity constraints in ACL is investigated showing ACL as a hybrid learning framework that integrates the explanatory (discriminant) and descriptive (characteristic) settings of ILP. | [
837,
2282,
2426
] | Train |
1,687 | 4 | Title: Markov games as a framework for multi-agent reinforcement learning
Abstract: In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic. | [
54,
148,
523,
773,
870,
898,
1137,
1189,
1228,
1459,
1557,
1632,
1649
] | Train |
1,688 | 1 | Title: Co-Evolving Soccer Softbot Team Coordination with Genetic Programming
Abstract: Genetic Programming is a promising new method for automatically generating functions and algorithms through natural selection. In contrast to other learning methods, Genetic Programming's automatic programming makes it a natural approach for developing algorithmic robot behaviors. In this paper we present an overview of how we apply Genetic Programming to behavior-based team coordination in the RoboCup Soccer Server domain. The result is not just a hand-coded soccer algorithm, but a team of softbots which have learned on their own how to play a reasonable game of soccer. | [
1178,
1228
] | Validation |
1,689 | 1 | Title: Selection for Wandering Behavior in a Small Robot
Abstract: We have evolved artificial neural networks to control the wandering behavior of small robots. The task was to touch as many squares in a grid as possible during a fixed period of time. A number of the simulated robots were embodied in small Lego (Trademark) robot, controlled by a Motorola (Trademark) 6811 processor; and their performance was compared to the simulations. We observed that: (a) evolution was an effective means to program control; (b) progress was characterized by sharply stepped periods of improvement, separated by periods of stasis that corresponded to levels of behavioral/computational complexity; and (c) the simulated and realized robots behaved quite similarly, the realized robots in some cases outperforming the simulated ones. Introducing random noise to the simulations improved the fit somewhat (from 0.73 to 0.79). Hybrid simulated/embodied selection regimes for evolutionary robots are discussed. | [
38,
163,
219,
538,
1738
] | Train |
1,690 | 1 | Title: Evolving Behavioral Strategies in Predators and Prey
Abstract: The predator/prey domain is utilized to conduct research in Distributed Artificial Intelligence. Genetic Programming is used to evolve behavioral strategies for the predator agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms. | [
995,
1178,
1736
] | Test |
1,691 | 1 | Title: Evolutionary Algorithms: Some Very Old Strategies for Optimization and Adaptation
Abstract: Genetic Algorithms and Evolution Strategies, the main representatives of a class of algorithms based on the model of natural evolution, are discussed w.r.t. their basic working mechanisms, differences, and application possibilities. The mechanism of self-adaptation of strategy parameters within Evolution Strategies is emphasized and turns out to be the major difference to Genetic Algorithms, since it allows for an on-line adaptation of strategy parameters without exogenous control. | [
163,
422,
793
] | Validation |
1,692 | 6 | Title: Boosting Trees for Cost-Sensitive Classifications
Abstract: This paper explores two boosting techniques for cost-sensitive tree classification in the situation where misclassification costs change very often. Ideally, one would like to have only one induction, and use the induced model for different misclassification costs. Thus, it demands robustness of the induced model against cost changes. Combining multiple trees gives robust predictions against this change. We demonstrate that ordinary boosting combined with the minimum expected cost criterion to select the prediction class is a good solution under this situation. We also introduce a variant of the ordinary boosting procedure which utilizes the cost information during training. We show that the proposed technique performs better than the ordinary boosting in terms of misclassification cost. However, this technique requires to induce a set of new trees every time the cost changes. Our empirical investigation also reveals some interesting behavior of boosting decision trees for cost-sensitive classification. | [
70,
228,
1484
] | Train |
1,693 | 4 | Title: INCREMENTAL SELF-IMPROVEMENT FOR LIFE-TIME MULTI-AGENT REINFORCEMENT LEARNING
Abstract: Previous approaches to multi-agent reinforcement learning are either very limited or heuristic by nature. The main reason is: each agent's or "animat's" environment continually changes because the other learning animats keep changing. Traditional reinforcement learning algorithms cannot properly deal with this. Their convergence theorems require repeatable trials and strong (typically Markovian) assumptions about the environment. In this paper, however, we use a novel, general, sound method for multiple, reinforcement learning "animats", each living a single life with limited computational resources in an unrestricted, changing environment. The method is called "incremental self-improvement" (IS | Schmidhuber, 1994). IS properly takes into account that whatever some animat learns at some point may affect learning conditions for other animats or for itself at any later point. The learning algorithm of an IS-based animat is embedded in its own policy | the animat cannot only improve its performance, but in principle also improve the way it improves etc. At certain times in the animat's life, IS uses reinforcement/time ratios to estimate from a single training example (namely the entire life so far) which previously learned things are still useful, and selectively keeps them but gets rid of those that start appearing harmful. IS is based on an efficient, stack-based backtracking procedure which is guaranteed to make each animat's learning history a history of long-term reinforcement accelerations. Experiments demonstrate IS' effectiveness. In one experiment, IS learns a sequence of more and more complex function approximation problems. In another, a multi-agent system consisting of three co-evolving, IS-based animats chasing each other learns interesting, stochastic predator and prey strategies. | [
1228
] | Validation |
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