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|---|---|---|---|---|
894 | 3 | Title: Bayesian Analysis of Agricultural Field Experiments
Abstract: SUMMARY The paper describes Bayesian analysis for agricultural field experiments, a topic that has received very little previous attention, despite a vast frequentist literature. Adoption of the Bayesian paradigm simplifies the interpretation of the results, especially in ranking and selection. Also, complex formulations can be analyzed with comparative ease, using Markov chain Monte Carlo methods. A key ingredient in the approach is the need for spatial representations of the unobserved fertility patterns. This is discussed in detail. Problems caused by outliers and by jumps in fertility are tackled via hierarchical-t formulations that may find use in other contexts. The paper includes three analyses of variety trials for yield and one example involving binary data; none is entirely straightforward. Some comparisons with frequentist analyses are made. The datasets are available at http://www.stat.duke.edu/~higdon/trials/data.html. | [
1255
] | Test |
895 | 3 | Title: CABeN: A Collection of Algorithms for Belief Networks Correspond with:
Abstract: Portions of this report have been published in the Proceedings of the Fifteenth Annual Symposium on Computer Applications in Medical Care (November, 1991). | [
1436
] | Train |
896 | 3 | Title: Discretization of continuous Markov chains and MCMC convergence assessment
Abstract: We show in this paper that continuous state space Markov chains can be rigorously discretized into finite Markov chains. The idea is to subsample the continuous chain at renewal times related to small sets which control the discretization. Once a finite Markov chain is derived from the MCMC output, general convergence properties on finite state spaces can be exploited for convergence assessment in several directions. Our choice is based on a divergence criterion derived from Kemeny and Snell (1960), which is first evaluated on parallel chains with a stopping time, and then implemented, more efficiently, on two parallel chains only, using Birkhoff's pointwise ergodic theorem for stopping rules. The performance of this criterion is illustrated on three standard examples. | [
468,
888,
1372
] | Train |
897 | 3 | Title: Parallel Markov chain Monte Carlo sampling.
Abstract: Markov chain Monte Carlo (MCMC) samplers have proved remarkably popular as tools for Bayesian computation. However, problems can arise in their application when the density of interest is high dimensional and strongly correlated. In these circumstances the sampler may be slow to traverse the state space and mixing is poor. In this article we offer a partial solution to this problem. The state space of the Markov chain is augmented to accommodate multiple chains in parallel. Updates to individual chains are based around a genetic style crossover operator acting on `parent' states drawn from the population of chains. This process makes efficient use of gradient information implicitly encoded within the distribution of states across the population. Empirical studies support the claim that the crossover operator acting on a parallel population of chains improves mixing. This is illustrated with an example of sampling a high dimensional posterior probability density from a complex predictive model. By adopting a latent variable approach the methodology is extended to deal with variable selection and model averaging in high dimensions. This is illustrated with an example of knot selection for a spline interpolant. | [
157,
1240
] | Train |
898 | 3 | Title: database
Abstract: MIT Computational Cognitive Science Technical Report 9701 Abstract We describe variational approximation methods for efficient probabilistic reasoning, applying these methods to the problem of diagnostic inference in the QMR-DT database. The QMR-DT database is a large-scale belief network based on statistical and expert knowledge in internal medicine. The size and complexity of this network render exact probabilistic diagnosis infeasible for all but a small set of cases. This has hindered the development of the QMR- DT network as a practical diagnostic tool and has hindered researchers from exploring and critiquing the diagnostic behavior of QMR. In this paper we describe how variational approximation methods can be applied to the QMR network, resulting in fast diagnostic inference. We evaluate the accuracy of our methods on a set of standard diagnostic cases and compare to stochastic sampling methods. | [
107,
108,
1687
] | Train |
899 | 2 | Title: GA-RBF: A Self-Optimising RBF Network
Abstract: The effects of a neural network's topology on its performance are well known, yet the question of finding optimal configurations automatically remains largely open. This paper proposes a solution to this problem for RBF networks. A self- optimising approach, driven by an evolutionary strategy, is taken. The algorithm uses output information and a computationally efficient approximation of RBF networks to optimise the K-means clustering process by co-evolving the two determinant parameters of the network's layout: the number of centroids and the centroids' positions. Empirical results demonstrate promise. | [
611,
1564,
1565,
1672
] | Train |
900 | 1 | Title: Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the
Abstract: This paper describes a hybrid methodology that integrates genetic algorithms and decision tree learning in order to evolve useful subsets of discriminatory features for recognizing complex visual concepts. A genetic algorithm (GA) is used to search the space of all possible subsets of a large set of candidate discrimination features. Candidate feature subsets are evaluated by using C4.5, a decision-tree learning algorithm, to produce a decision tree based on the given features using a limited amount of training data. The classification performance of the resulting decision tree on unseen testing data is used as the fitness of the underlying feature subset. Experimental results are presented to show how increasing the amount of learning significantly improves feature set evolution for difficult visual recognition problems involving satellite and facial image data. In addition, we also report on the extent to which other more subtle aspects of the Baldwin effect are exhibited by the system. | [
228,
1207,
1351,
1533,
1583
] | Train |
901 | 0 | Title: Evaluating Computational Assistance for Crisis Response
Abstract: In this paper we examine the behavior of a human-computer system for crisis response. As one instance of crisis management, we describe the task of responding to spills and fires involving hazardous materials. We then describe INCA, an intelligent assistant for planning and scheduling in this domain, and its relation to human users. We focus on INCA's strategy of retrieving a case from a case library, seeding the initial schedule, and then helping the user adapt this seed. We also present three hypotheses about the behavior of this mixed-initiative system and some experiments designed to test them. The results suggest that our approach leads to faster response development than user-generated or automatically-generated schedules but without sacrificing solution quality. | [
1497,
1553,
1554
] | Validation |
902 | 1 | Title: Explanations of Empirically Derived Reactive Plans
Abstract: Given an adequate simulation model of the task environment and payoff function that measures the quality of partially successful plans, competition-based heuristics such as genetic algorithms can develop high performance reactive rules for interesting sequential decision tasks. We have previously described an implemented system, called SAMUEL, for learning reactive plans and have shown that the system can successfully learn rules for a laboratory scale tactical problem. In this paper, we describe a method for deriving explanations to justify the success of such empirically derived rule sets. The method consists of inferring plausible subgoals and then explaining how the reactive rules trigger a sequence of actions (i.e., a stra tegy) to satisfy the subgoals. | [
910,
964,
965,
966,
981,
1174
] | Test |
903 | 6 | Title: Learning Concepts from Sensor Data of a Mobile Robot
Abstract: Machine learning can be a most valuable tool for improving the flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the low-level representations of sensing and action and the high-level representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm grdt has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user defines in terms of grammars. | [
1491,
1672
] | Test |
904 | 3 | Title: Assessing Convergence of Markov Chain Monte Carlo Algorithms
Abstract: We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular emphasis on implementational issues and possible extensions. The methods are compared in terms of their interpretability and applicability and recommendations are provided for particular classes of problems. | [
115,
292,
352,
889,
892,
1372,
1733
] | Validation |
905 | 3 | Title: Compositional Modeling With DPNs
Abstract: We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular emphasis on implementational issues and possible extensions. The methods are compared in terms of their interpretability and applicability and recommendations are provided for particular classes of problems. | [
558,
976,
1287,
1393,
1397
] | Train |
906 | 2 | Title: Memory-based Time Series Recognition A New Methodology and Real World Applications
Abstract: We motivate the use of convergence diagnostic techniques for Markov Chain Monte Carlo algorithms and review various methods proposed in the MCMC literature. A common notation is established and each method is discussed with particular emphasis on implementational issues and possible extensions. The methods are compared in terms of their interpretability and applicability and recommendations are provided for particular classes of problems. | [
74,
1019,
1860
] | Train |
907 | 2 | Title: Visual Tracking of Moving Objects using a Neural Network Controller
Abstract: For a target tracking task, the hand-held camera of the anthropomorphic OSCAR-robot manipulator has to track an object which moves arbitrarily on a table. The desired camera-joint mapping is approximated by a feedforward neural network. Through the use of time derivatives of the position of the object and of the manipulator, the controller can inherently predict the next position of the moving target object. In this paper several `anticipative' controllers are described, and successfully applied to track a moving object. | [
1252
] | Validation |
908 | 2 | Title: Eclectic Machine Learning
Abstract: For a target tracking task, the hand-held camera of the anthropomorphic OSCAR-robot manipulator has to track an object which moves arbitrarily on a table. The desired camera-joint mapping is approximated by a feedforward neural network. Through the use of time derivatives of the position of the object and of the manipulator, the controller can inherently predict the next position of the moving target object. In this paper several `anticipative' controllers are described, and successfully applied to track a moving object. | [
414,
809,
919,
2304
] | Test |
909 | 3 | Title: Regression Can Build Predictive Causal Models
Abstract: Covariance information can help an algorithm search for predictive causal models and estimate the strengths of causal relationships. This information should not be discarded after conditional independence constraints are identified, as is usual in contemporary causal induction algorithms. Our fbd algorithm combines covariance information with an effective heuristic to build predictive causal models. We demonstrate that fbd is accurate and efficient. In one experiment we assess fbd's ability to find the best predictors for variables; in another we compare its performance, using many measures, with Pearl and Verma's ic algorithm. And although fbd is based on multiple linear regression, we cite evidence that it performs well on problems that are very difficult for regression algorithms. | [
827,
913,
1527,
1894
] | Train |
910 | 1 | Title: Learning Sequential Decision Rules Using Simulation Models and Competition
Abstract: The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical decision rules from a simple flight simulator. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Several experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. | [
163,
523,
565,
764,
811,
902,
964,
966,
981,
982,
1117,
1131,
1140,
1221,
1253,
1311,
1432,
1481,
1573,
1589,
1590,
1673
] | Train |
911 | 6 | Title: Utilizing Prior Concepts for Learning
Abstract: The inductive learning problem consists of learning a concept given examples and non-examples of the concept. To perform this learning task, inductive learning algorithms bias their learning method. Here we discuss biasing the learning method to use previously learned concepts from the same domain. These learned concepts highlight useful information for other concepts in the domain. We describe a transference bias and present M-FOCL, a Horn clause relational learning algorithm, that utilizes this bias to learn multiple concepts. We provide preliminary empirical evaluation to show the effects of biasing previous information on noise-free and noisy data. | [
177,
585,
1354
] | Validation |
912 | 2 | Title: Statistical Ideas for Selecting Network Architectures
Abstract: Choosing the architecture of a neural network is one of the most important problems in making neural networks practically useful, but accounts of applications usually sweep these details under the carpet. How many hidden units are needed? Should weight decay be used, and if so how much? What type of output units should be chosen? And so on. We address these issues within the framework of statistical theory for model This paper is principally concerned with architecture selection issues for feed-forward neural networks (also known as multi-layer perceptrons). Many of the same issues arise in selecting radial basis function networks, recurrent networks and more widely. These problems occur in a much wider context within statistics, and applied statisticians have been selecting and combining models for decades. Two recent discussions are [4, 5]. References [3, 20, 21, 22] discuss neural networks from a statistical perspective. choice, which provides a number of workable approximate answers. | [
1149,
1150,
1240,
1241
] | Validation |
913 | 3 | Title: A Statistical Semantics for Causation Key words: causality, induction, learning
Abstract: We propose a model-theoretic definition of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covari-ations following standard norms of inductive reasoning. We also establish a complete characterization of the conditions under which such a distinction is possible. Finally, we provide a proof-theoretical procedure for inductive causation and show that, for a large class of data and structures, effective algorithms exist that uncover the direction of causal influences as defined above. | [
827,
909
] | Train |
914 | 2 | Title: All-to-all Broadcast on the CNS-1
Abstract: This study deals with the all-to-all broadcast on the CNS-1. We determine a lower bound for the run time and present an algorithm meeting this bound. Since this study points out a bottleneck in the network interface, we also analyze the performance of alternative interface designs. Our analyses are based on a run time model of the network. | [
272,
1551
] | Train |
915 | 3 | Title: Abstract
Abstract: Automated decision making is often complicated by the complexity of the knowledge involved. Much of this complexity arises from the context-sensitive variations of the underlying phenomena. We propose a framework for representing descriptive, context-sensitive knowledge. Our approach attempts to integrate categorical and uncertain knowledge in a network formalism. This paper outlines the basic representation constructs, examines their expressiveness and efficiency, and discusses the potential applications of the framework. | [
1172,
1294
] | Validation |
916 | 2 | Title: A comparison of some error estimates for neural network models Summary
Abstract: We discuss a number of methods for estimating the standard error of predicted values from a multi-layer perceptron. These methods include the delta method based on the Hessian, bootstrap estimators, and the "sandwich" estimator. The methods are described and compared in a number of examples. We find that the bootstrap methods perform best, partly because they capture variability due to the choice of starting weights. | [
157,
427,
1038,
2373,
2374,
2686
] | Train |
917 | 3 | Title: Practical Bayesian Inference Using Mixtures of Mixtures
Abstract: Discrete mixtures of normal distributions are widely used in modeling amplitude fluctuations of electrical potentials at synapses of human, and other animal nervous systems. The usual framework has independent data values y j arising as y j = j + x n 0 +j where the means j come from some discrete prior G() and the unknown x n 0 +j 's and observed x j ; j = 1; : : : ; n 0 are gaussian noise terms. A practically important development of the associated statistical methods is the issue of non-normality of the noise terms, often the norm rather than the exception in the neurological context. We have recently developed models, based on convolutions of Dirichlet process mixtures, for such problems. Explicitly, we model the noise data values x j as arising from a Dirich-let process mixture of normals, in addition to modeling the location prior G() as a Dirichlet process itself. This induces a Dirichlet mixture of mixtures of normals, whose analysis may be developed using Gibbs sampling techniques. We discuss these models and their analysis, and illustrate in the context of neurological response analysis. | [
784,
850,
1338
] | Train |
918 | 2 | Title: Predictive Robot Control with Neural Networks
Abstract: Neural controllers are able to position the hand-held camera of the (3DOF) anthropomorphic OSCAR-robot manipulator above an object which is arbitrary placed on a table. The desired camera-joint mapping is approximated by feedforward neural networks. However, if the object is moving, the manipulator lags behind because of the required time to preprocess the visual information and to move the manipulator. Through the use of time derivatives of the position of the object and of the manipulator, the controller can inherently predict the next position of the object. In this paper several `predictive' controllers are proposed, and successfully applied to track a moving object. | [
1252
] | Train |
919 | 2 | Title: A Generalizing Adaptive Discriminant Network
Abstract: This paper overviews the AA1 (Adaptive Algorithm 1) model of ASOCS the (Adaptive Self - Organizing Concurrent Systems) approach. It also presents promising empirical generalization results of AA1 with actual data. AA1 is a topologically dynamic network which grows to fit the problem being learned. AA1 generalizes in a self-organizing fashion to a network which seeks to find features which discriminate between concepts. Convergence to a training set is both guaranteed and bounded linearly in time. | [
809,
908,
1129
] | Validation |
920 | 2 | Title: Maximum likelihood source separation for discrete sources
Abstract: This communication deals with the source separation problem which consists in the separation of a noisy mixture of independent sources without a priori knowledge of the mixture coefficients. In this paper, we consider the maximum likelihood (ML) approach for discrete source signals with known probability distributions. An important feature of the ML approach in Gaussian noise is that the covariance matrix of the additive noise can be treated as a parameter. Hence, it is not necessary to know or to model the spatial structure of the noise. Another striking feature offered in the case of discrete sources is that, under mild assumptions, it is possible to separate more sources than sensors. In this paper, we consider maximization of the likelihood via the Expectation-Maximization (EM) algorithm. | [
873,
1520
] | Validation |
921 | 2 | Title: DIAGNOSING AND CORRECTING SYSTEM ANOMALIES WITH A ROBUST CLASSIFIER
Abstract: If a robust statistical model has been developed to classify the ``health'' of a system, a well-known Taylor series approximation technique forms the basis of a diagnostic/recovery procedure that can be initiated when the system's health degrades or fails altogether. This procedure determines a ranked set of probable causes for the degraded health state, which can be used as a prioritized checklist for isolating system anomalies and quantifying corrective action. The diagnostic/recovery procedure is applicable to any classifier known to be robust; it can be applied to both neural network and traditional parametric pattern classifiers generated by a supervised learning procedure in which an empirical risk/benefit measure is optimized. We describe the procedure mathematically and demonstrate its ability to detect and diagnose the cause(s) of faults in NASA's Deep Space Communications Complex at Goldstone, California. | [
1265,
1725
] | Train |
922 | 0 | Title: Towards Improving Case Adaptability with a Genetic Algorithm
Abstract: Case combination is a difficult problem in Case Based Reasoning, as sub-cases often exhibit conflicts when merged together. In our previous work we formalized case combination by representing each case as a constraint satisfaction problem, and used the minimum conflicts algorithm to systematically synthesize the global solution. However, we also found instances of the problem in which the minimum conflicts algorithm does not perform case combination efficiently. In this paper we describe those situations in which initially retrieved cases are not easily adaptable, and propose a method by which to improve case adaptability with a genetic algorithm. We introduce a fitness function that maintains as much retrieved case information as possible, while also perturbing a sub-solution to allow subsequent case combination to proceed more efficiently. | [
580,
923,
1233,
1416
] | Validation |
923 | 0 | Title: Dynamic Constraint Satisfaction using Case-Based Reasoning Techniques
Abstract: The Dynamic Constraint Satisfaction Problem (DCSP) formalism has been gaining attention as a valuable and often necessary extension of the static CSP framework. Dynamic Constraint Satisfaction enables CSP techniques to be applied more extensively, since it can be applied in domains where the set of constraints and variables involved in the problem evolves with time. At the same time, the Case-Based Reasoning (CBR) community has been working on techniques by which to reuse existing solutions when solving new problems. We have observed that dynamic constraint satisfaction matches very closely the case-based reasoning process of case adaptation. These observations emerged from our previous work on combining CBR and CSP to achieve a constraint-based adaptation. This paper summarizes our previous results, describes the similarity of the challenges facing both DCSP and case adaptation, and shows how CSP and CBR can together begin to address these chal lenges. | [
922,
1126,
1233,
1416
] | Train |
924 | 6 | Title: Quantifying Prior Determination Knowledge using the PAC Learning Model
Abstract: Prior knowledge, or bias, regarding a concept can speed up the task of learning it. Probably Approximately Correct (PAC) learning is a mathematical model of concept learning that can be used to quantify the speed up due to different forms of bias on learning. Thus far, PAC learning has mostly been used to analyze syntactic bias, such as limiting concepts to conjunctions of boolean prepositions. This paper demonstrates that PAC learning can also be used to analyze semantic bias, such as a domain theory about the concept being learned. The key idea is to view the hypothesis space in PAC learning as that consistent with all prior knowledge, syntactic and semantic. In particular, the paper presents a PAC analysis of determinations, a type of relevance knowledge. The results of the analysis reveal crisp distinctions and relations among different determinations, and illustrate the usefulness of an analysis based on the PAC model. | [
672,
1309,
1460,
1480,
1539
] | Train |
925 | 2 | Title: Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration
Abstract: Computational models of natural systems often contain free parameters that must be set to optimize the predictive accuracy of the models. This process| called calibration|can be viewed as a form of supervised learning in the presence of prior knowledge. In this view, the fixed aspects of the model constitute the prior knowledge, and the goal is to learn values for the free parameters. We report on a series of attempts to learn parameter values for a global vegetation model called MAPSS (Mapped Atmosphere-Plant-Soil System) developed by our collaborator, Ron Neilson. Standard machine learning methods do not work with MAPSS, because the constraints introduced by the structure of the model create a very difficult non-linear optimization problem. We developed a new divide-and-conquer approach in which subsets of the parameters are calibrated while others are held constant. This approach succeeds because it is possible to select training examples that exercise only portions of the model. | [
1532
] | Test |
926 | 6 | Title: Virtual Seens and the Frequently Used Dataset
Abstract: The paper considers the situation in which a learner's testing set contains close approximations of cases which appear in the training set. Such cases can be considered `virtual seens' since they are approximately seen by the learner. Generalisation measures which do not take account of the frequency of virtual seens may be misleading. The paper shows that the 1-NN algorithm can be used to derive a normalising baseline for gen-eralisation statistics. The normalisation process is demonstrated though application to Holte's [1] study in which the generalisation performance of the 1R algorithm was tested against C4.5 on 16 commonly used datasets. | [
1019,
1112
] | Train |
927 | 0 | Title: Exemplar-based Music Structure Recognition
Abstract: We tend to think of what we really know as what we can talk about, and disparage knowledge that we can't verbalize. [Dowling 1989, p. 252] | [
1019,
1328
] | Train |
928 | 0 | Title: Learning to Refine Case Libraries:
Abstract: Initial Results Abstract. Conversational case-based reasoning (CBR) systems, which incrementally extract a query description through a user-directed conversation, are advertised for their ease of use. However, designing large case libraries that have good performance (i.e., precision and querying efficiency) is difficult. CBR vendors provide guidelines for designing these libraries manually, but the guidelines are difficult to apply. We describe an automated inductive approach that revises conversational case libraries to increase their conformance with design guidelines. Revision increased performance on three conversational case libraries. | [
256,
983,
1636
] | Test |
929 | 3 | Title: In: A Mixture Model System for Medical and Machine Diagnosis
Abstract: Diagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The joint probability distribution of the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can also be handled correctly. The request for new information to refine the diagnosis is performed using the maximum utility principle. Since the system is based on learning it is domain independent and less labor intensive than expert systems or probabilistic networks. An example using a heart disease database is presented. | [
71,
740,
1559,
1697,
2442
] | Test |
930 | 2 | Title: BACKPROPAGATION CAN GIVE RISE TO SPURIOUS LOCAL MINIMA EVEN FOR NETWORKS WITHOUT HIDDEN LAYERS
Abstract: We give an example of a neural net without hidden layers and with a sigmoid transfer function, together with a training set of binary vectors, for which the sum of the squared errors, regarded as a function of the weights, has a local minimum which is not a global minimum. The example consists of a set of 125 training instances, with four weights and a threshold to be learnt. We do not know if substantially smaller binary examples exist. | [
805,
1062,
1254
] | Train |
931 | 6 | Title: MAJORITY VOTE CLASSIFIERS: THEORY AND APPLICATIONS
Abstract: We give an example of a neural net without hidden layers and with a sigmoid transfer function, together with a training set of binary vectors, for which the sum of the squared errors, regarded as a function of the weights, has a local minimum which is not a global minimum. The example consists of a set of 125 training instances, with four weights and a threshold to be learnt. We do not know if substantially smaller binary examples exist. | [
70,
1000,
1053,
1463,
1484
] | Test |
932 | 6 | Title: Learning an Optimally Accurate Representational System
Abstract: The multiple extension problem arises because a default theory can use different subsets of its defaults to propose different, mutually incompatible, answers to some queries. This paper presents an algorithm that uses a set of observations to learn a credulous version of this default theory that is (essentially) "optimally accurate". In more detail, we can associate a given default theory with a set of related credulous theories R = fR i g, where each R i uses its own total ordering of the defaults to determine which single answer to return for each query. Our goal is to select the credulous theory that has the highest "expected accuracy", where each R i 's expected accuracy is the probability that the answer it produces to a query will correspond correctly to the world. Unfortunately, a theory's expected accuracy depends on the distribution of queries, which is usually not known. Moreover, the task of identifying the optimal R opt 2 R, even given that distribution information, is intractable. This paper presents a method, OptAcc, that sidesteps these problems by using a set of samples to estimate the unknown distribution, and by hill-climbing to a local optimum. In particular, given any parameters *; ffi > 0, OptAcc produces an R oa 2 R whose expected accuracy is, with probability at least 1 ffi, within * of a local optimum. Appeared in ECAI Workshop on Theoretical Foundations of Knowledge Representation and Reasoning, | [
251,
865,
1505
] | Test |
933 | 4 | Title: Learning an Optimally Accurate Representational System
Abstract: Multigrid Q-Learning Charles W. Anderson and Stewart G. Crawford-Hines Technical Report CS-94-121 October 11, 1994 | [
483,
875
] | Train |
934 | 1 | Title: Complexity Compression and Evolution
Abstract: Compression of information is an important concept in the theory of learning. We argue for the hypothesis that there is an inherent compression pressure towards short, elegant and general solutions in a genetic programming system and other variable length evolutionary algorithms. This pressure becomes visible if the size or complexity of solutions are measured without non-effective code segments called introns. The built in parsimony pressure effects complex fitness functions, crossover probability, generality, maximum depth or length of solutions, explicit parsimony, granularity of fitness function, initialization depth or length, and modulariz-ation. Some of these effects are positive and some are negative. In this work we provide a basis for an analysis of these effects and suggestions to overcome the negative implications in order to obtain the balance needed for successful evolution. An empirical investigation that supports our hypothesis is also presented. | [
380,
844,
860,
940,
1009,
1353,
1631
] | Validation |
935 | 1 | Title: Complexity Compression and Evolution
Abstract: CBR Assisted Explanation of GA Results Computer Science Technical Report number 361 CRCC Technical Report number 63 | [
163,
1136
] | Train |
936 | 4 | Title: XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions more complex
Abstract: Wilson's recent XCS classifier system forms complete mappings of the payoff environment in the reinforcement learning tradition thanks to its accuracy based fitness. According to Wilson's Generalization Hypothesis, XCS has a tendency towards generalization. With the XCS Optimality Hypothesis, I suggest that XCS systems can evolve optimal populations (representations); populations which accurately map all input/action pairs to payoff predictions using the smallest possible set of non-overlapping classifiers. The ability of XCS to evolve optimal populations for boolean multiplexer problems is demonstrated using condensation, a technique in which evolutionary search is suspended by setting the crossover and mutation rates to zero. Condensation is automatically triggered by self-monitoring of performance statistics, and the entire learning process is terminated by autotermination. Combined, these techniques allow a classifier system to evolve optimal representations of boolean functions without any form of supervision. | [
1515
] | Train |
937 | 0 | Title: The RISE System: Conquering Without Separating
Abstract: Current rule induction systems (e.g. CN2) typically rely on a "separate and conquer" strategy, learning each rule only from still-uncovered examples. This results in a dwindling number of examples being available for learning successive rules, adversely affecting the system's accuracy. An alternative is to learn all rules simultaneously, using the entire training set for each. This approach is implemented in the Rise 1.0 system. Empirical comparison of Rise with CN2 suggests that "conquering without separating" performs similarly to its counterpart in simple domains, but achieves increasingly substantial gains in accuracy as the domain difficulty grows. | [
426,
1234
] | Train |
938 | 1 | Title: Genetic Programming of Minimal Neural Nets Using Occam's Razor
Abstract: A genetic programming method is investigated for optimizing both the architecture and the connection weights of multilayer feedforward neural networks. The genotype of each network is represented as a tree whose depth and width are dynamically adapted to the particular application by specifically defined genetic operators. The weights are trained by a next-ascent hillclimb-ing search. A new fitness function is proposed that quantifies the principle of Occam's razor. It makes an optimal trade-off between the error fitting ability and the parsimony of the network. We discuss the results for two problems of differing complexity and study the convergence and scaling properties of the algorithm. | [
560,
611,
1127,
2196
] | Train |
939 | 2 | Title: A Simple Neural Network Models Categorical Perception of Facial Expressions
Abstract: The performance of a neural network that categorizes facial expressions is compared with human subjects over a set of experiments using interpolated imagery. The experiments for both the human subjects and neural networks make use of interpolations of facial expressions from the Pictures of Facial Affect Database [Ekman and Friesen, 1976]. The only difference in materials between those used in the human subjects experiments [Young et al., 1997] and our materials are the manner in which the interpolated images are constructed - image-quality morphs versus pixel averages. Nevertheless, the neural network accurately captures the categorical nature of the human responses, showing sharp transitions in labeling of images along the interpolated sequence. Crucially for a demonstration of categorical perception [Harnad, 1987], the model shows the highest discrimination between transition images at the crossover point. The model also captures the shape of the reaction time curves of the human subjects along the sequences. Finally, the network matches human subjects' judgements of which expressions are being mixed in the images. The main failing of the model is that there are intrusions of neutral responses in some transitions, which are not seen in the human subjects. We attribute this difference to the difference between the pixel average stimuli and the image quality morph stimuli. These results show that a simple neural network classifier, with no access to the biological constraints that are presumably imposed on the human emotion processor, and whose only access to the surrounding culture is the category labels placed by American subjects on the facial expressions, can nevertheless simulate fairly well the human responses to emotional expressions. | [
1242
] | Train |
940 | 1 | Title: Signal Path Oriented Approach for Generation of Dynamic Process Models
Abstract: The article at hand discusses a tool for automatic generation of structured models for complex dynamic processes by means of genetic programming. In contrast to other techniques which use genetic programming to find an appropriate arithmetic expression in order to describe the input-output behaviour of a process, this tool is based on a block oriented approach with a transparent description of signal paths. A short survey on other techniques for computer based system identification is given and the basic concept of SMOG (Structured MOdel Generator) is described. Furthermore latest extensions of the system are presented in detail, including automatically defined sub-models and quali tative fitness criteria. | [
163,
934
] | Train |
941 | 1 | Title: Hyperplane Ranking in Simple Genetic Algorithms
Abstract: We examine the role of hyperplane ranking during search performed by a simple genetic algorithm. We also develop a metric for measuring the degree of ranking that exists with respect to static measurements taken directly from the function, as well as the measurement of dynamic ranking of hyperplanes during genetic search. We show that the degree of dynamic ranking induced by a simple genetic algorithm is highly correlated with the degree of static ranking that is inherent in the function, especially during the initial genera tions of search. | [
1441,
1717
] | Test |
942 | 1 | Title: Genetic Programming Methodology, Parallelization and Applications par
Abstract: We examine the role of hyperplane ranking during search performed by a simple genetic algorithm. We also develop a metric for measuring the degree of ranking that exists with respect to static measurements taken directly from the function, as well as the measurement of dynamic ranking of hyperplanes during genetic search. We show that the degree of dynamic ranking induced by a simple genetic algorithm is highly correlated with the degree of static ranking that is inherent in the function, especially during the initial genera tions of search. | [
163,
1153
] | Train |
943 | 1 | Title: Crossover or Mutation?
Abstract: Genetic algorithms rely on two genetic operators crossover and mutation. Although there exists a large body of conventional wisdom concerning the roles of crossover and mutation, these roles have not been captured in a theoretical fashion. For example, it has never been theoretically shown that mutation is in some sense "less powerful" than crossover or vice versa. This paper provides some answers to these questions by theoretically demonstrating that there are some important characteristics of each operator that are not captured by the other. | [
728,
793,
1016,
1466,
1646
] | Validation |
944 | 3 | Title: Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting
Abstract: In a recent paper, Friedman, Geiger, and Goldszmidt [8] introduced a classifier based on Bayesian networks, called Tree Augmented Naive Bayes (TAN), that outperforms naive Bayes and performs competitively with C4.5 and other state-of-the-art methods. This classifier has several advantages including robustness and polynomial computational complexity. One limitation of the TAN classifier is that it applies only to discrete attributes, and thus, continuous attributes must be prediscretized. In this paper, we extend TAN to deal with continuous attributes directly via parametric (e.g., Gaussians) and semiparametric (e.g., mixture of Gaussians) conditional probabilities. The result is a classifier that can represent and combine both discrete and continuous attributes. In addition, we propose a new method that takes advantage of the modeling language of Bayesian networks in order to represent attributes both in discrete and continuous form simultaneously, and use both versions in the classification. This automates the process of deciding which form of the attribute is most relevant to the classification task. It also avoids the commitment to either a discretized or a (semi)parametric form, since different attributes may correlate better with one version or the other. Our empirical results show that this latter method usually achieves classification performance that is as good as or better than either the purely discrete or the purely continuous TAN models. | [
1335,
1337
] | Train |
945 | 3 | Title: Structured Representation of Complex Stochastic Systems
Abstract: This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes. Unfortunately, they are often unwieldy since they cannot explicitly model the complex organizational structure of many real life systems: the fact that processes are typically composed of several interacting subprocesses, each of which can, in turn, be further decomposed. We propose a hierarchically structured representation language which extends both dynamic Bayesian networks and the object-oriented Bayesian network framework of [9], and show that our language allows us to describe such systems in a natural and modular way. Our language supports a natural representation for certain system characteristics that are hard to capture using more traditional frameworks. For example, it allows us to represent systems where some processes evolve at a different rate than others, or systems where the processes interact only intermittently. We provide a simple inference mechanism for our representation via translation to Bayesian networks, and suggest ways in which the inference algorithm can exploit the additional structure encoded in our representation. | [
62,
327,
788,
1287,
1414
] | Test |
946 | 2 | Title: Constructive Learning of Recurrent Neural Networks: Limitations of Recurrent Casade Correlation and a Simple Solution
Abstract: It is often difficult to predict the optimal neural network size for a particular application. Constructive or destructive methods that add or subtract neurons, layers, connections, etc. might offer a solution to this problem. We prove that one method, Recurrent Cascade Correlation, due to its topology, has fundamental limitations in representation and thus in its learning capabilities. It cannot represent with monotone (i.e. sigmoid) and hard-threshold activation functions certain finite state automata. We give a "preliminary" approach on how to get around these limitations by devising a simple constructive training method that adds neurons during training while still preserving the powerful fully-recurrent structure. We illustrate this approach by simulations which learn many examples of regular grammars that the | [
427,
1179,
1600
] | Train |
947 | 0 | Title: An Optimal Weighting Criterion of Case Indexing for Both Numeric and Symbolic Attributes
Abstract: Indexing of cases is an important topic for Memory-Based Reasoning(MBR). One key problem is how to assign weights to attributes of cases. Although several weighting methods have been proposed, some methods cannot handle numeric attributes directly, so it is necessary to discretize numeric values by classification. Furthermore, existing methods have no theoretical background, so little can be said about optimality. We propose a new weighting method based on a statistical technique called Quantification Method II. It can handle both numeric and symbolic attributes in the same framework. Generated attribute weights are optimal in the sense that they maximize the ratio of variance between classes to variance of all cases. Experiments on several benchmark tests show that in many cases, our method obtains higher accuracies than some other weighting methods. The results also indicate that it can distinguish relevant attributes from irrelevant ones, and can tolerate noisy data. | [
1328
] | Validation |
948 | 2 | Title: An Optimal Weighting Criterion of Case Indexing for Both Numeric and Symbolic Attributes
Abstract: A General Result on the Stabilization of Linear Systems Using Bounded Controls 1 ABSTRACT We present two constructions of controllers that globally stabilize linear systems subject to control saturation. We allow essentially arbitrary saturation functions. The only conditions imposed on the system are the obvious necessary ones, namely that no eigenvalues of the uncontrolled system have positive real part and that the standard stabilizability rank condition hold. One of the constructions is in terms of a "neural-network type" one-hidden layer architecture, while the other one is in terms of cascades of linear maps and saturations. | [
1281,
1282,
1446
] | Train |
949 | 2 | Title: Classifying Seismic Signals by Integrating Ensembles of Neural Networks
Abstract: This paper proposes a classification scheme based on integration of multiple Ensembles of ANNs. It is demonstrated on a classification problem, in which seismic signals of Natural Earthquakes must be distinguished from seismic signals of Artificial Explosions. A Redundant Classification Environment consists of several Ensembles of Neural Networks is created and trained on Bootstrap Sample Sets, using various data representations and architectures. The ANNs within the Ensembles are aggregated (as in Bagging) while the Ensembles are integrated non-linearly, in a signal adaptive manner, using a posterior confidence measure based on the agreement (variance) within the Ensembles. The proposed Integrated Classification Machine achieved 92.1% correct classifications on the seismic test data. Cross Validation evaluations and comparisons indicate that such integration of a collection of ANN's Ensembles is a robust way for handling high dimensional problems with a complex non-stationary signal space as in the current Seismic Classification problem. | [
74,
1512,
1608
] | Train |
950 | 3 | Title: Model Selection for Generalized Linear Models via GLIB, with Application to Epidemiology 1
Abstract: 1 This is the first draft of a chapter for Bayesian Biostatistics, edited by Donald A. Berry and Darlene K. Strangl. Adrian E. Raftery is Professor of Statistics and Sociology, Department of Statistics, GN-22, University of Washington, Seattle, WA 98195, USA. Sylvia Richardson is Directeur de Recherche, INSERM Unite 170, 16 avenue Paul Vaillant Couturier, 94807 Villejuif CEDEX, France. Raftery's research was supported by ONR contract no. N-00014-91-J-1074, by the Ministere de la Recherche et de l'Espace, Paris, by the Universite de Paris VI, and by INRIA, Rocquencourt, France. Raftery thanks the latter two institutions, Paul Deheuvels and Gilles Celeux for hearty hospitality during his Paris sabbatical in which part of this chapter was written. The authors are grateful to Christine Montfort for excellent research assistance and to Mariette Gerber, Michel Chavance and David Madigan for helpful discussions. | [
84,
1240,
1241
] | Test |
951 | 0 | Title: Using Case-Based Reasoning to Acquire User Scheduling Preferences that Change over Time
Abstract: Production/Manufacturing scheduling typically involves the acquisition of user optimization preferences. The ill-structuredness of both the problem space and the desired objectives make practical scheduling problems difficult to formalize and costly to solve, especially when problem configurations and user optimization preferences change over time. This paper advocates an incremental revision framework for improving schedule quality and incorporating user dynamically changing preferences through Case-Based Reasoning. Our implemented system, called CABINS, records situation-dependent tradeoffs and consequences that result from schedule revision to guide schedule improvement. The preliminary experimental results show that CABINS is able to effectively capture both user static and dynamic preferences which are not known to the system and only exist implicitly in a extensional manner in the case base. | [
1401,
1554,
2605
] | Train |
952 | 3 | Title: Some Varieties of Qualitative Probability
Abstract: | [
1064,
1660
] | Test |
953 | 1 | Title: Behavior Hierarchy for Autonomous Mobile Robots: Fuzzy-behavior modulation and evolution
Abstract: Realization of autonomous behavior in mobile robots, using fuzzy logic control, requires formulation of rules which are collectively responsible for necessary levels of intelligence. Such a collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This article describes how this can be done using a behavior-based architecture. A behavior hierarchy and mechanisms of control decision-making are described. In addition, an approach to behavior coordination is described with emphasis on evolution of fuzzy coordination rules using the genetic programming (GP) paradigm. Both conventional GP and steady-state GP are applied to evolve a fuzzy-behavior for sensor-based goal-seeking. The usefulness of the behavior hierarchy, and partial design by GP, is evident in performance results of simulated autonomous navigation. | [
972
] | Train |
954 | 3 | Title: Unsupervised learning of distributions on binary vectors using two layer networks
Abstract: We present a distribution model for binary vectors, called the influence combination model and show how this model can be used as the basis for unsupervised learning algorithms for feature selection. The model is closely related to the Harmonium model defined by Smolensky [RM86][Ch.6]. In the first part of the paper we analyze properties of this distribution representation scheme. We show that arbitrary distributions of binary vectors can be approximated by the combination model. We show how the weight vectors in the model can be interpreted as high order correlation patterns among the input bits. We compare the combination model with the mixture model and with principle component analysis. In the second part of the paper we present two algorithms for learning the combination model from examples. The first algorithm is based on gradient ascent. Here we give a closed form for this gradient that is significantly easier to compute than the corresponding gradient for the general Boltzmann machine. The second learning algorithm is a greedy method that creates the hidden units and computes their weights one at a time. This method is a variant of projection pursuit density estimation. In the third part of the paper we give experimental results for these learning methods on synthetic data and on natural data of handwritten digit images. | [
427,
450,
969,
1461,
1591
] | Test |
955 | 6 | Title: Separating Formal Bounds from Practical Performance in Learning Systems
Abstract: We present a distribution model for binary vectors, called the influence combination model and show how this model can be used as the basis for unsupervised learning algorithms for feature selection. The model is closely related to the Harmonium model defined by Smolensky [RM86][Ch.6]. In the first part of the paper we analyze properties of this distribution representation scheme. We show that arbitrary distributions of binary vectors can be approximated by the combination model. We show how the weight vectors in the model can be interpreted as high order correlation patterns among the input bits. We compare the combination model with the mixture model and with principle component analysis. In the second part of the paper we present two algorithms for learning the combination model from examples. The first algorithm is based on gradient ascent. Here we give a closed form for this gradient that is significantly easier to compute than the corresponding gradient for the general Boltzmann machine. The second learning algorithm is a greedy method that creates the hidden units and computes their weights one at a time. This method is a variant of projection pursuit density estimation. In the third part of the paper we give experimental results for these learning methods on synthetic data and on natural data of handwritten digit images. | [
109,
560,
1280
] | Train |
956 | 1 | Title: Modeling Distributed Search via Social Insects
Abstract: Complex group behavior arises in social insects colonies as the integration of the actions of simple and redundant individual insects [Adler and Gordon, 1992, Oster and Wilson, 1978]. Furthermore, the colony can act as an information center to expedite foraging [Brown, 1989]. We apply these lessons from natural systems to model collective action and memory in a computational agent society. Collective action can expedite search in combinatorial optimization problems [Dorigo et al., 1996]. Collective memory can improve learning in multi-agent systems [Garland and Alterman, 1996]. Our collective adaptation integrates the simplicity of collective action with the pattern detection of collective memory to significantly improve both the gathering and processing of knowledge. As a test of the role of the society as an information center, we examine the ability of the society to distribute task allocation without any omnipotent centralized control. | [
995,
1178,
1231,
2598
] | Test |
957 | 6 | Title: ANNEALED THEORIES OF LEARNING
Abstract: We study annealed theories of learning boolean functions using a concept class of finite cardinality. The naive annealed theory can be used to derive a universal learning curve bound for zero temperature learning, similar to the inverse square root bound from the Vapnik-Chervonenkis theory. Tighter, nonuniversal learning curve bounds are also derived. A more refined annealed theory leads to still tighter bounds, which in some cases are very similar to results previously obtained using one-step replica symmetry breaking. | [
967
] | Train |
958 | 1 | Title: The Evolution of Memory and Mental Models Using Genetic Programming build internal representations of their
Abstract: This paper applies genetic programming their successive actions. The results show to the evolution of intelligent agents that | [
1409
] | Train |
959 | 1 | Title: Numerical techniques for efficient sonar bearing and range searching in the near field using genetic algorithms
Abstract: This article describes a numerical method that may be used to efficiently locate and track underwater sonar targets in the near-field, with both bearing and range estimation, for the case of very large passive arrays. The approach used has no requirement for a priori knowledge about the source and uses only limited information about the receiver array shape. The role of sensor position uncertainty and the consequence of targets always being in the near-field are analysed and the problems associated with the manipulation of large matrices inherent in conventional eigenvalue type algorithms noted. A simpler numerical approach is then presented which reduces the problem to that of search optimization. When using this method the location of a target corresponds to finding the position of the maximum weighted sum of the output from all sensors. Since this search procedure can be dealt with using modern stochastic optimization methods, such as the genetic algorithm, the operational requirement that an acceptable accuracy be achieved in real time can usually be met. The array studied here consists of 225 elements positioned along a flexible cable towed behind a ship with 3.4m between sensors, giving an effective aperture of 761.6m. For such a long array, the far field assumption used in most beam-forming algorithms is no longer appropriate. The waves emitted by the targets then have to be considered as curved rather than plane. It is shown that, for simulated data, if no significant noise | [
793,
1130
] | Validation |
960 | 5 | Title: Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Constructive Induction-based Learning Agents:
Abstract: This paper introduces a new type of intelligent agent called a constructive induction-based learning agent (CILA). This agent differs from other adaptive agents because it has the ability to not only learn how to assist a user in some task, but also to incrementally adapt its knowledge representation space to better fit the given learning task. The agents ability to autonomously make problem-oriented modifications to the originally given representation space is due to its constructive induction (CI) learning method. Selective induction (SI) learning methods, and agents based on these methods, rely on a good representation space. A good representation space has no misclassification noise, inter-correlated attributes or irrelevant attributes. Our proposed CILA has methods for overcoming all of these problems. In agent domains with poor representations, the CI-based learning agent will learn more accurate rules and be more useful than an SI-based learning agent. This paper gives an architecture for a CI-based learning agent and gives an empirical comparison of a CI and SI for a set of six abstract domains involving DNF-type (disjunctive normal form) descriptions. | [
378,
1292,
1301
] | Train |
961 | 1 | Title: CFS-C: A Package of Domain Independent Subroutines for Implementing Classifier Systems in Arbitrary, User-Defined Environments.
Abstract: This paper introduces a new type of intelligent agent called a constructive induction-based learning agent (CILA). This agent differs from other adaptive agents because it has the ability to not only learn how to assist a user in some task, but also to incrementally adapt its knowledge representation space to better fit the given learning task. The agents ability to autonomously make problem-oriented modifications to the originally given representation space is due to its constructive induction (CI) learning method. Selective induction (SI) learning methods, and agents based on these methods, rely on a good representation space. A good representation space has no misclassification noise, inter-correlated attributes or irrelevant attributes. Our proposed CILA has methods for overcoming all of these problems. In agent domains with poor representations, the CI-based learning agent will learn more accurate rules and be more useful than an SI-based learning agent. This paper gives an architecture for a CI-based learning agent and gives an empirical comparison of a CI and SI for a set of six abstract domains involving DNF-type (disjunctive normal form) descriptions. | [
163,
523,
1515
] | Train |
962 | 2 | Title: Using Many-Particle Decomposition to get a Parallel Self-Organising Map
Abstract: We propose a method for decreasing the computational complexity of self-organising maps. The method uses a partitioning of the neurons into disjoint clusters. Teaching of the neurons occurs on a cluster-basis instead of on a neuron-basis. For teaching an N-neuron network with N 0 samples, the computational complexity decreases from O(N 0 N) to O(N 0 log N). Furthermore, we introduce a measure for the amount of order in a self-organising map, and show that the introduced algorithm behaves as well as the original algorithm. | [
745,
829
] | Train |
963 | 5 | Title: Cooperation of Data-driven and Model-based Induction Methods for Relational Learning
Abstract: Inductive learning in relational domains has been shown to be intractable in general. Many approaches to this task have been suggested nevertheless; all in some way restrict the hypothesis space searched. They can be roughly divided into two groups: data-driven, where the restriction is encoded into the algorithm, and model-based, where the restrictions are made more or less explicit with some form of declarative bias. This paper describes Incy, an inductive learner that seeks to combine aspects of both approaches. Incy is initially data-driven, using examples and background knowledge to put forth and specialize hypotheses based on the "connectivity" of the data at hand. It is model-driven in that hypotheses are abstracted into rule models, which are used both for control decisions in the data-driven phase and for model-guided induction. Key Words: Inductive learning in relational domains, cooperation of data-driven and model-guided methods, implicit and declarative bias. | [
344,
1519
] | Train |
964 | 1 | Title: Simulation-Assisted Learning by Competition: Effects of Noise Differences Between Training Model and Target Environment
Abstract: The problem of learning decision rules for sequential tasks is addressed, focusing on the problem of learning tactical plans from a simple flight simulator where a plane must avoid a missile. The learning method relies on the notion of competition and employs genetic algorithms to search the space of decision policies. Experiments are presented that address issues arising from differences between the simulation model on which learning occurs and the target environment on which the decision rules are ultimately tested. Specifically, either the model or the target environment may contain noise. These experiments examine the effect of learning tactical plans without noise and then testing the plans in a noisy environment, and the effect of learning plans in a noisy simulator and then testing the plans in a noise-free environment. Empirical results show that, while best result are obtained when the training model closely matches the target environment, using a training environment that is more noisy than the target environment is better than using using a training environment that has less noise than the target environment. | [
902,
910,
965,
966,
1140,
1432,
1481,
1673
] | Train |
965 | 1 | Title: Improving Tactical Plans with Genetic Algorithms
Abstract: | [
177,
902,
964,
966,
981,
1060,
1140,
1253,
1432,
1481,
1590,
1673
] | Train |
966 | 1 | Title: Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation
Abstract: Navigation through obstacles such as mine fields is an important capability for autonomous underwater vehicles. One way to produce robust behavior is to perform projective planning. However, real-time performance is a critical requirement in navigation. What is needed for a truly autonomous vehicle are robust reactive rules that perform well in a wide variety of situations, and that also achieve real-time performance. In this work, SAMUEL, a learning system based on genetic algorithms, is used to learn high-performance reactive strategies for navigation and collision avoidance. | [
163,
902,
910,
964,
965,
1131,
1140,
1481,
1673
] | Train |
967 | 6 | Title: Rigorous Learning Curve Bounds from Statistical Mechanics
Abstract: In this paper we introduce and investigate a mathematically rigorous theory of learning curves that is based on ideas from statistical mechanics. The advantage of our theory over the well-established Vapnik-Chervonenkis theory is that our bounds can be considerably tighter in many cases, and are also more reflective of the true behavior (functional form) of learning curves. This behavior can often exhibit dramatic properties such as phase transitions, as well as power law asymptotics not explained by the VC theory. The disadvantages of our theory are that its application requires knowledge of the input distribution, and it is limited so far to finite cardinality function classes. We illustrate our results with many concrete examples of learning curve bounds derived from our theory. | [
56,
57,
109,
306,
322,
689,
847,
848,
957,
1032,
1394,
1400,
1561
] | Test |
968 | 2 | Title: Using Prior Knowledge in an NNPDA to Learn Context-Free Languages
Abstract: Although considerable interest has been shown in language inference and automata induction using recurrent neural networks, success of these models has mostly been limited to regular languages. We have previously demonstrated that Neural Network Pushdown Automaton (NNPDA) model is capable of learning deterministic context-free languages (e.g., a n b n and parenthesis languages) from examples. However, the learning task is computationally intensive. In this paper we discuss some ways in which a priori knowledge about the task and data could be used for efficient learning. We also observe that such knowledge is often an experimental prerequisite for learning nontrivial languages (eg. a n b n cb m a m ). | [
1285
] | Validation |
969 | 3 | Title: Learning Stochastic Feedforward Networks
Abstract: Connectionist learning procedures are presented for "sigmoid" and "noisy-OR" varieties of stochastic feedforward network. These networks are in the same class as the "belief networks" used in expert systems. They represent a probability distribution over a set of visible variables using hidden variables to express correlations. Conditional probability distributions can be exhibited by stochastic simulation for use in tasks such as classification. Learning from empirical data is done via a gradient-ascent method analogous to that used in Boltzmann machines, but due to the feedforward nature of the connections, the negative phase of Boltzmann machine learning is unnecessary. Experimental results show that, as a result, learning in a sigmoid feedforward network can be faster than in a Boltzmann machine. These networks have other advantages over Boltzmann machines in pattern classification and decision making applications, and provide a link between work on connectionist learning and work on the representation of expert knowledge. | [
954
] | Validation |
970 | 1 | Title: Generality versus Size in Genetic Programming
Abstract: Genetic Programming (GP) uses variable size representations as programs. Size becomes an important and interesting emergent property of the structures evolved by GP. The size of programs can be both a controlling and a controlled factor in GP search. Size influences the efficiency of the search process and is related to the generality of solutions. This paper analyzes the size and generality issues in standard GP and GP using subroutines and addresses the question whether such an analysis can help control the search process. We relate the size, generalization and modularity issues for programs evolved to control an agent in a dynamic and non-deterministic environment, as exemplified by the Pac-Man game. | [
567,
974,
1378,
1396,
1533
] | Train |
971 | 3 | Title: Decision-Theoretic Foundations for Causal Reasoning
Abstract: We present a definition of cause and effect in terms of decision-theoretic primitives and thereby provide a principled foundation for causal reasoning. Our definition departs from the traditional view of causation in that causal assertions may vary with the set of decisions available. We argue that this approach provides added clarity to the notion of cause. Also in this paper, we examine the encoding of causal relationships in directed acyclic graphs. We describe a special class of influence diagrams, those in canonical form, and show its relationship to Pearl's representation of cause and effect. Finally, we show how canonical form facilitates counterfactual reasoning. | [
1326,
1527,
1602,
2166
] | Train |
972 | 1 | Title: On Genetic Programming of Fuzzy Rule-Based Systems for Intelligent Control
Abstract: Fuzzy logic and evolutionary computation have proven to be convenient tools for handling real-world uncertainty and designing control systems, respectively. An approach is presented that combines attributes of these paradigms for the purpose of developing intelligent control systems. The potential of the genetic programming paradigm (GP) for learning rules for use in fuzzy logic controllers (FLCs) is evaluated by focussing on the problem of discovering a controller for mobile robot path tracking. Performance results of incomplete rule-bases compare favorably to those of a complete FLC designed by the usual trial-and-error approach. A constrained syntactic representation supported by structure-preserving genetic operators is also introduced. | [
953
] | Test |
973 | 3 | Title: Interval Censored Survival Data: A Review of Recent Progress
Abstract: We review estimation in interval censoring models, including nonparametric estimation of a distribution function and estimation of regression models. In the non-parametric setting, we describe computational procedures and asymptotic properties of the nonparametric maximum likelihood estimators. In the regression setting, we focus on the proportional hazards, the proportional odds and the accelerated failure time semiparametric regression models. Particular emphasis is given to calculation of the Fisher information for the regression parameters. We also discuss computation of the regression parameter estimators via profile likelihood or maximization of the semi-parametric likelihood, distributional results for the maximum likelihood estimators, and estimation of (asymptotic) variances. Some further problems and open questions are also reviewed. | [
802,
993
] | Test |
974 | 1 | Title: Balancing Accuracy and Parsimony in Genetic Programming 1
Abstract: Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this paper we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks. | [
844,
970
] | Train |
975 | 2 | Title: State Reconstruction for Determining Predictability in Driven Nonlinear Acoustical Systems
Abstract: Genetic programming is distinguished from other evolutionary algorithms in that it uses tree representations of variable size instead of linear strings of fixed length. The flexible representation scheme is very important because it allows the underlying structure of the data to be discovered automatically. One primary difficulty, however, is that the solutions may grow too big without any improvement of their generalization ability. In this paper we investigate the fundamental relationship between the performance and complexity of the evolved structures. The essence of the parsimony problem is demonstrated empirically by analyzing error landscapes of programs evolved for neural network synthesis. We consider genetic programming as a statistical inference problem and apply the Bayesian model-comparison framework to introduce a class of fitness functions with error and complexity terms. An adaptive learning method is then presented that automatically balances the model-complexity factor to evolve parsimonious programs without losing the diversity of the population needed for achieving the desired training accuracy. The effectiveness of this approach is empirically shown on the induction of sigma-pi neural networks for solving a real-world medical diagnosis problem as well as benchmark tasks. | [
74,
76,
608,
668,
1079
] | Test |
976 | 3 | Title: Space-efficient inference in dynamic probabilistic networks
Abstract: Dynamic probabilistic networks (DPNs) are a useful tool for modeling complex stochastic processes. The simplest inference task in DPNs is monitoring | that is, computing a posterior distribution for the state variables at each time step given all observations up to that time. Recursive, constant-space algorithms are well-known for monitoring in DPNs and other models. This paper is concerned with hindsight | that is, computing a posterior distribution given both past and future observations. Hindsight is an essential subtask of learning DPN models from data. Existing algorithms for hindsight in DPNs use O(SN ) space and time, where N is the total length of the observation sequence and S is the state space size for each time step. They are therefore impractical for hindsight in complex models with long observation sequences. This paper presents an O(S log N ) space, O(SN log N ) time hindsight algorithm. We demonstrates the effectiveness of the algorithm in two real-world DPN learning problems. We also discuss the possibility of an O(S)-space, O(SN )-time algorithm. | [
905,
1268,
1287,
1393
] | Test |
977 | 6 | Title: Pessimistic and Optimistic Induction
Abstract: Learning methods vary in the optimism or pessimism with which they regard the informativeness of learned knowledge. Pessimism is implicit in hypothesis testing, where we wish to draw cautious conclusions from experimental evidence. However, this paper demonstrates that optimism in the utility of derived rules may be the preferred bias for learning systems themselves. We examine the continuum between naive pessimism and naive optimism in the context of a decision tree learner that prunes rules based on stringent (i.e., pessimistic) or weak (i.e., optimistic) tests of their significance. Our experimental results indicate that in most cases optimism is preferred, but particularly in cases of sparse training data and high noise. This work generalizes earlier findings by Fisher and Schlimmer (1988) and Schaffer (1992), and we discuss its relevance to unsupervised learning, small disjuncts, and other issues. | [
1234
] | Train |
978 | 2 | Title: Hierarchical Recurrent Neural Networks for Long-Term Dependencies
Abstract: We have already shown that extracting long-term dependencies from sequential data is difficult, both for deterministic dynamical systems such as recurrent networks, and probabilistic models such as hidden Markov models (HMMs) or input/output hidden Markov models (IOHMMs). In practice, to avoid this problem, researchers have used domain specific a-priori knowledge to give meaning to the hidden or state variables representing past context. In this paper, we propose to use a more general type of a-priori knowledge, namely that the temporal dependencies are structured hierarchically. This implies that long-term dependencies are represented by variables with a long time scale. This principle is applied to a recurrent network which includes delays and multiple time scales. Experiments confirm the advantages of such structures. A similar approach is proposed for HMMs and IOHMMs. | [
664,
1593,
1825,
1954
] | Train |
979 | 2 | Title: FLAT MINIMA Neural Computation 9(1):1-42 (1997)
Abstract: We present a new algorithm for finding low complexity neural networks with high generalization capability. The algorithm searches for a "flat" minimum of the error function. A flat minimum is a large connected region in weight-space where the error remains approximately constant. An MDL-based, Bayesian argument suggests that flat minima correspond to "simple" networks and low expected overfitting. The argument is based on a Gibbs algorithm variant and a novel way of splitting generalization error into underfitting and overfitting error. Unlike many previous approaches, ours does not require Gaussian assumptions and does not depend on a "good" weight prior instead we have a prior over input/output functions, thus taking into account net architecture and training set. Although our algorithm requires the computation of second order derivatives, it has backprop's order of complexity. Automatically, it effectively prunes units, weights, and input lines. Various experiments with feedforward and recurrent nets are described. In an application to stock market prediction, flat minimum search outperforms (1) conventional backprop, (2) weight decay, (3) "optimal brain surgeon" / "optimal brain damage". We also provide pseudo code of the algorithm (omitted from the NC-version). | [
68,
157,
766,
879,
1825,
1845
] | Train |
980 | 2 | Title: Some Topics in Neural Networks and Control
Abstract: We present a new algorithm for finding low complexity neural networks with high generalization capability. The algorithm searches for a "flat" minimum of the error function. A flat minimum is a large connected region in weight-space where the error remains approximately constant. An MDL-based, Bayesian argument suggests that flat minima correspond to "simple" networks and low expected overfitting. The argument is based on a Gibbs algorithm variant and a novel way of splitting generalization error into underfitting and overfitting error. Unlike many previous approaches, ours does not require Gaussian assumptions and does not depend on a "good" weight prior instead we have a prior over input/output functions, thus taking into account net architecture and training set. Although our algorithm requires the computation of second order derivatives, it has backprop's order of complexity. Automatically, it effectively prunes units, weights, and input lines. Various experiments with feedforward and recurrent nets are described. In an application to stock market prediction, flat minimum search outperforms (1) conventional backprop, (2) weight decay, (3) "optimal brain surgeon" / "optimal brain damage". We also provide pseudo code of the algorithm (omitted from the NC-version). | [
1488
] | Train |
981 | 0 | Title: AN ENHANCER FOR REACTIVE PLANS
Abstract: This paper describes our method for improving the comprehensibility, accuracy, and generality of reactive plans. A reactive plan is a set of reactive rules. Our method involves two phases: (1) formulate explanations of execution traces, and then (2) generate new reactive rules from the explanations. Since the explanation phase has been previously described, the primary focus of this paper is the rule generation phase. This latter phase consists of taking a subset of the explanations and using these explanations to generate a set of new reactive rules to add to the original set. The particular subset of the explanations that is chosen yields rules that provide new domain knowledge for handling knowledge gaps in the original rule set. The original rule set, in a complimentary manner, provides expertise to fill the gaps where the domain knowledge provided by the new rules is incomplete. | [
902,
910,
965,
1140
] | Train |
982 | 1 | Title: Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems
Abstract: Technical Report AI94-218 May 1994 Abstract A new method for developing good value-ordering strategies in constraint satisfaction search is presented. Using an evolutionary technique called SANE, in which individual neurons evolve to cooperate and form a neural network, problem-specific knowledge can be discovered that results in better value-ordering decisions than those based on problem-general heuristics. A neural network was evolved in a chronological backtrack search to decide the ordering of cars in a resource-limited assembly line. The network required 1/30 of the backtracks of random ordering and 1/3 of the backtracks of the maximization of future options heuristic. The SANE approach should extend well to other domains where heuristic information is either difficult to discover or problem-specific. | [
163,
247,
910
] | Train |
983 | 0 | Title: Refining Conversational Case Libraries
Abstract: Conversational case-based reasoning (CBR) shells (e.g., Inference's CBR Express) are commercially successful tools for supporting the development of help desk and related applications. In contrast to rule-based expert systems, they capture knowledge as cases rather than more problematic rules, and they can be incrementally extended. However, rather than eliminate the knowledge engineering bottleneck, they refocus it on case engineering, the task of carefully authoring cases according to library design guidelines to ensure good performance. Designing complex libraries according to these guidelines is difficult; software is needed to assist users with case authoring. We describe an approach for revising case libraries according to design guidelines, its implementation in Clire, and empirical results showing that, under some conditions, this approach can improve conversational CBR performance. | [
256,
887,
928,
1002,
1302,
1531,
1636,
1735
] | Train |
984 | 0 | Title: A Computational Account of Movement Learning and its Impact on the Speed-Accuracy Tradeoff
Abstract: We present a computational model of movement skill learning. The types of skills addressed are a class of trajectory following movements involving multiple accelerations, decelerations and changes in direction and lasting more than a few seconds. These skills are acquired through observation and improved through practice. We also review the speed-accuracy tradeoff|one of the most robust phenomena in human motor behavior. We present two speed-accuracy tradeoff experiments where the model's performance fits human behavior quite well. | [
1048
] | Train |
985 | 3 | Title: Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases
Abstract: We present a computational model of movement skill learning. The types of skills addressed are a class of trajectory following movements involving multiple accelerations, decelerations and changes in direction and lasting more than a few seconds. These skills are acquired through observation and improved through practice. We also review the speed-accuracy tradeoff|one of the most robust phenomena in human motor behavior. We present two speed-accuracy tradeoff experiments where the model's performance fits human behavior quite well. | [
159,
1102,
2038,
2066
] | Train |
986 | 0 | Title: Improving accuracy by combining rule-based and case-based reasoning
Abstract: An architecture is presented for combining rule-based and case-based reasoning. The architecture is intended for domains that are understood reasonably well, but still imperfectly. It uses a set of rules, which are taken to be only approximately correct, to obtain a preliminary answer for a given problem; it then draws analogies from cases to handle exceptions to the rules. Having rules together with cases not only increases the architecture's domain coverage, it also allows innovative ways of doing case-based reasoning: the same rules that are used for rule-based reasoning are also used by the case-based component to do case indexing and case adaptation. The architecture was applied to the task of name pronunciation, and, with minimal knowledge engineering, was found to perform almost at the level of the best commercial systems. Moreover, its accuracy was found to exceed what it could have achieved with rules or cases alone, thus demonstrating the accuracy improvement afforded by combining rule-based and case-based reasoning. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories of Cambridge, Massachusetts; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories. All rights reserved. | [
136,
862,
1642,
1644,
2484,
2605,
2614,
2616
] | Train |
987 | 3 | Title: Stacked Density Estimation
Abstract: Technical Report No. 97-36, Information and Computer Science Department, University of California, Irvine | [
74,
1240,
1608
] | Train |
988 | 4 | Title: A COMPARISON OF Q-LEARNING AND CLASSIFIER SYSTEMS
Abstract: Reinforcement Learning is a class of problems in which an autonomous agent acting in a given environment improves its behavior by progressively maximizing a function calculated just on the basis of a succession of scalar responses received from the environment. Q-learning and classifier systems (CS) are two methods among the most used to solve reinforcement learning problems. Notwithstanding their popularity and their shared goal, they have been in the past often considered as two different models. In this paper we first show that the classifier system, when restricted to a sharp simplification called discounted max very simple classifier system (D MAX - VSCS), boils down to tabular Q-learning. It follows that D MAX -VSCS converges to the optimal policy as proved by Watkins & Dayan (1992), and that it can draw profit from the results of experimental and theoretical works dedicated to improve Q-learning and to facilitate its use in concrete applications. In the second part of the paper, we show that three of the restrictions we need to impose to the CS for deriving its equivalence with Q-learning, that is, no internal states, no don't care symbols, and no structural changes, turn out so essential as to be recently rediscovered and reprogrammed by Q-learning adepts. Eventually, we sketch further similarities among ongoing work within both research contexts. The main contribution of the paper is therefore to make explicit the strong similarities existing between Q-learning and classifier systems, and to show that experience gained with research within one domain can be useful to direct future research in the other one. | [
1515
] | Train |
989 | 2 | Title: Finding Compact and Sparse Distributed Representations of Visual Images
Abstract: Some recent work has investigated the dichotomy between compact coding using dimensionality reduction and sparse distributed coding in the context of understanding biological information processing. We introduce an artificial neural network which self organises on the basis of simple Hebbian learning and negative feedback of activation and show that it is capable of both forming compact codings of data distributions and also of identifying filters most sensitive to sparse distributed codes. The network is extremely simple and its biological relevance is investigated via its response to a set of images which are typical of everyday life. However, an analysis of the network's identification of the filter for sparse coding reveals that this coding may not be globally optimal and that there exists an innate limiting factor which cannot be transcended. | [
1068,
1418
] | Train |
990 | 2 | Title: Shattering all sets of k points in "general position" requires (k 1)=2 parameters
Abstract: For classes of concepts defined by certain classes of analytic functions depending on n parameters, there are nonempty open sets of samples of length 2n + 2 which cannot be shattered. A slighly weaker result is also proved for piecewise-analytic functions. The special case of neural networks is discussed. | [
58,
805,
1774
] | Test |
991 | 0 | Title: Systematic Evaluation of Design Decisions in CBR Systems
Abstract: Two important goals in the evaluation of an AI theory or model are to assess the merit of the design decisions in the performance of an implemented computer system and to analyze the impact in the performance when the system faces problem domains with different characteristics. This is particularly difficult in case-based reasoning systems because such systems are typically very complex, as are the tasks and domains in which they operate. We present a methodology for the evaluation of case-based reasoning systems through systematic empirical experimentation over a range of system configurations and environmental conditions, coupled with rigorous statistical analysis of the results of the experiments. This methodology enables us to understand the behavior of the system in terms of the theory and design of the computational model, to select the best system configuration for a given domain, and to predict how the system will behave in response to changing domain and problem characteristics. A case study of a mul-tistrategy case-based and reinforcement learning system which performs autonomous robotic navigation is presented as an example. | [
318,
858,
1084,
1368
] | Train |
992 | 0 | Title: Adapting Abstract Knowledge
Abstract: For a case-based reasoner to use its knowledge flexibly, it must be equipped with a powerful case adapter. A case-based reasoner can only cope with variation in the form of the problems it is given to the extent that its cases in memory can be efficiently adapted to fit a wide range of new situations. In this paper, we address the task of adapting abstract knowledge about planning to fit specific planning situations. First we show that adapting abstract cases requires reconciling incommensurate representations of planning situations. Next, we describe a representation system, a memory organization, and an adaptation process tailored to this requirement. Our approach is implemented in brainstormer, a planner that takes abstract advice. | [
1354
] | Train |
993 | 3 | Title: Efficient Estimation for the Cox Model with Interval Censoring
Abstract: The maximum likelihood estimator (MLE) for the proportional hazards model with current status data is studied. It is shown that the MLE for the regression parameter is asymptotically normal with p n-convergence rate and achieves the information bound, even though the MLE for the baseline cumulative hazard function only converges at n 1=3 rate. Estimation of the asymptotic variance matrix for the MLE of the regression parameter is also considered. To prove our main results, we also establish a general theorem showing that the MLE of the finite dimensional parameter in a class of semiparametric models is asymptotically efficient even though the MLE of the infinite dimensional parameter converges at a rate slower than The results are illustrated by applying them to a data set from a tumoriginicity study. 1. Introduction In many survival analysis problems, we are interested in the p | [
973
] | Validation |