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|---|---|---|---|---|
1,994 | 3 | Title: Constructive Belief and Rational Representation
Abstract: It is commonplace in artificial intelligence to divide an agent's explicit beliefs into two parts: the beliefs explicitly represented or manifest in memory, and the implicitly represented or constructive beliefs that are repeatedly reconstructed when needed rather than memorized. Many theories of knowledge view the relation between manifest and constructive beliefs as a logical relation, with the manifest beliefs representing the constructive beliefs through a logic of belief. This view, however, limits the ability of a theory to treat incomplete or inconsistent sets of beliefs in useful ways. We argue that a more illuminating view is that belief is the result of rational representation. In this theory, the agent obtains its constructive beliefs by using its manifest beliefs and preferences to rationally (in the sense of decision theory) choose the most useful conclusions indicated by the manifest beliefs. | [
1800,
1995,
2097,
2241
] | Validation |
1,995 | 3 | Title: Rationality and its Roles in Reasoning
Abstract: The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, influence the design and analysis of reasoning and representation systems. | [
1800,
1907,
1994,
2097
] | Train |
1,996 | 2 | Title: A New Algorithm for DNA Sequence Assembly Running Title: A New Algorithm for DNA Sequence Assembly
Abstract: The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, influence the design and analysis of reasoning and representation systems. | [
1997
] | Train |
1,997 | 2 | Title: AMASS: A Structured Pattern Matching Approach to Shotgun Sequence Assembly
Abstract: In this paper, we propose an efficient, reliable shotgun sequence assembly algorithm based on a fingerprinting scheme that is robust to both noise and repetitive sequences in the data. Our algorithm uses exact matches of short patterns randomly selected from fragment data to identify fragment overlaps, construct an overlap map, and finally deliver a consensus sequence. We show how statistical clues made explicit in our approach can easily be exploited to correctly assemble results even in the presence of extensive repetitive sequences. Our approach is exceptionally fast in practice: e.g., we have successfully assembled a whole Mycoplasma genitalium genome (approximately 580 kbps) in roughly 8 minutes of 64MB 200MHz Pentium Pro CPU time from real shotgun data, where most existing algorithms can be expected to run for several hours to a day on the same data. Moreover, experiments with shotgun data synthetically prepared from real DNA sequences from a wide range of organisms (including human DNA) and containing extensive repeating regions demonstrate our algorithm's robustness to noise and the presence of repetitive sequences. For example, we have correctly assembled a 238kbp Human DNA sequence in less than 3 minutes of 64MB 200MHz Pentium Pro CPU time. fl Support for this research was provided in part by the Office of Naval Research through grant N0014-94-1-1178. | [
1996
] | Train |
1,998 | 2 | Title: Programming Environment for a High Performance Parallel Supercomputer with Intelligent Communication
Abstract: At the Electronics Lab of the Swiss Federal Institute of Techology (ETH) in Zurich, the high performance Parallel Supercomputer MUSIC (MUlti processor System with Intelligent Communication) has beed developed. As applications in neural network simulation and molecular dynamics show, the Electronics Lab Supercomputer is absolutely on a par with those of conventional supercomputers, but electric power requirements are reduced by a factor of 1000, wight is reduced by a factor of 400 and price is reduced by a factor of 100. Software development is a key using such a parallel system. This report focus on the programming environment of the MUSIC system and on it's applications. | [
1873
] | Train |
1,999 | 1 | Title: Evolutionary Training of CLP-Constrained Neural Networks
Abstract: The paper is concerned with the integration of constraint logic programming systems (CLP) with systems based on genetic algorithms (GA). The resulting framework is tailored for applications that require a first phase in which a number of constraints need to be generated, and a second phase in which an optimal solution satisfying these constraints is produced. The first phase is carried by the CLP and the second one by the GA. We present a specific framework where ECL i PS e (ECRC Common Logic Programming System) and GENOCOP (GEnetic algorithm for Numerical Optimization for COnstrained Problems) are integrated in a framework called CoCo (COmputational intelligence plus COnstraint logic programming). The CoCo system is applied to the training problem for neural networks. We consider constrained networks, e.g. neural networks with shared weights, constraints on the weights for example domain constraints for hardware implementation etc. Then ECL i PS e is used to generate the chromosome representation together with other constraints which ensure, in most cases, that each network is specified by exactly one chromosome. Thus the problem becomes a constrained optimization problem, where the optimization criterion is to optimize the error of the network, and GENOCOP is used to find an optimal solution. Note: The work of the second author was partially supported by SION, a department of the NWO, the National Foundation for Scientific Research. This work has been carried out while the third author was visiting CWI, Amsterdam, and the fourth author was visiting Leiden University. | [
427,
2003,
2515
] | Train |
2,000 | 3 | Title: A LOGICAL APPROACH TO REASONING ABOUT UNCERTAINTY: A TUTORIAL
Abstract: fl This paper will appear in Discourse, Interaction, and Communication, X. Arrazola, K. Korta, and F. J. Pelletier, eds., Kluwer, 1997. Much of this work was performed while the author was at IBM Almaden Research Center. IBM's support is gratefully acknowledged. | [
467,
2115
] | Train |
2,001 | 1 | Title: Comparison of the SAW-ing Evolutionary Algorithm and the Grouping Genetic Algorithm for Graph Coloring 1
Abstract: 1 This report is also available through http://www.wi.leidenuniv.nl/~gusz/ sawvsgga.ps.gz | [
833,
1796
] | Train |
2,002 | 3 | Title: Geometric Ergodicity of Gibbs and Block Gibbs Samplers for a Hierarchical Random Effects Model
Abstract: 1 This report is also available through http://www.wi.leidenuniv.nl/~gusz/ sawvsgga.ps.gz | [
1713,
2153,
2510
] | Validation |
2,003 | 1 | Title: Constraining of Weights using Regularities
Abstract: In this paper we study how global optimization methods (like genetic algorithms) can be used to train neural networks. We introduce the notion of regularity, for studying properties of the error function that expand the search space in an artificial way. Regularities are used to generate constraints on the weights of the network. In order to find a satisfiable set of constraints we use a constraint logic programming system. Then the training of the network becomes a constrained optimization problem. We also relate the notion of regularity to so-called network transformations. | [
1999,
2515
] | Test |
2,004 | 6 | Title: On Learning Bounded-Width Branching Programs
Abstract: In this paper, we study PAC-learning algorithms for specialized classes of deterministic finite automata (DFA). In particular, we study branching programs, and we investigate the influence of the width of the branching program on the difficulty of the learning problem. We first present a distribution-free algorithm for learning width-2 branching programs. We also give an algorithm for the proper learning of width-2 branching programs under uniform distribution on labeled samples. We then show that the existence of an efficient algorithm for learning width-3 branching programs would imply the existence of an efficient algorithm for learning DNF, which is not known to be the case. Finally, we show that the existence of an algorithm for learning width-3 branching programs would also yield an algorithm for learning a very restricted version of parity with noise. | [
672,
2040,
2360
] | Train |
2,005 | 6 | Title: The Parameterized Complexity of Sequence Alignment and Consensus
Abstract: The Longest common subsequence problem is examined from the point of view of parameterized computational complexity. There are several different ways in which parameters enter the problem, such as the number of sequences to be analyzed, the length of the common subsequence, and the size of the alphabet. Lower bounds on the complexity of this basic problem imply lower bounds on a number of other sequence alignment and consensus problems. At issue in the theory of parameterized complexity is whether a problem which takes input (x; k) can be solved in time f (k) n ff where ff is independent of k (termed fixed-parameter tractability). It can be argued that this is the appropriate asymptotic model of feasible computability for problems for which a small range of parameter values covers important applications | a situation which certainly holds for many problems in biological sequence analysis. Our main results show that: (1) The Longest Common Subsequence (LCS) parameterized by the number of sequences to be analyzed is hard for W [t] for all t. (2) The LCS problem problem, parameterized by the length of the common subsequence, belongs to W [P ] and is hard for W [2]. (3) The LCS problem parameterized both by the number of sequences and the length of the common subsequence, is complete for W [1]. All of the above results are obtained for unrestricted alphabet sizes. For alphabets of a fixed size, problems (2) and (3) are fixed-parameter tractable. We conjecture that (1) remains hard. | [
2345
] | Train |
2,006 | 6 | Title: Constructing New Attributes for Decision Tree Learning
Abstract: The Longest common subsequence problem is examined from the point of view of parameterized computational complexity. There are several different ways in which parameters enter the problem, such as the number of sequences to be analyzed, the length of the common subsequence, and the size of the alphabet. Lower bounds on the complexity of this basic problem imply lower bounds on a number of other sequence alignment and consensus problems. At issue in the theory of parameterized complexity is whether a problem which takes input (x; k) can be solved in time f (k) n ff where ff is independent of k (termed fixed-parameter tractability). It can be argued that this is the appropriate asymptotic model of feasible computability for problems for which a small range of parameter values covers important applications | a situation which certainly holds for many problems in biological sequence analysis. Our main results show that: (1) The Longest Common Subsequence (LCS) parameterized by the number of sequences to be analyzed is hard for W [t] for all t. (2) The LCS problem problem, parameterized by the length of the common subsequence, belongs to W [P ] and is hard for W [2]. (3) The LCS problem parameterized both by the number of sequences and the length of the common subsequence, is complete for W [1]. All of the above results are obtained for unrestricted alphabet sizes. For alphabets of a fixed size, problems (2) and (3) are fixed-parameter tractable. We conjecture that (1) remains hard. | [
1824
] | Test |
2,007 | 4 | Title: A Computer Scientist's View of Life, the Universe, and Everything
Abstract: Is the universe computable? If so, it may be much cheaper in terms of information requirements to compute all computable universes instead of just ours. I apply basic concepts of Kolmogorov complexity theory to the set of possible universes, and chat about perceived and true randomness, life, generalization, and learning in a given universe. Assumptions. A long time ago, the Great Programmer wrote a program that runs all possible universes on His Big Computer. "Possible" means "computable": (1) Each universe evolves on a discrete time scale. (2) Any universe's state at a given time is describable by a finite number of bits. One of the many universes is ours, despite some who evolved in it and claim it is incomputable. Computable universes. Let T M denote an arbitrary universal Turing machine with unidirectional output tape. T M 's input and output symbols are "0", "1", and "," (comma). T M 's possible input programs can be ordered alphabetically: "" (empty program), "0", "1", ",", "00", "01", "0,", "10", "11", "1,", ",0", ",1", ",,", "000", etc. Let A k denote T M 's k-th program in this list. Its output will be a finite or infinite string over the alphabet f "0","1",","g. This sequence of bitstrings separated by commas will be interpreted as the evolution E k of universe U k . If E k includes at least one comma, then let U l k represents U k 's state at the l-th time step of E k (k; l 2 f1; 2; : : : ; g). E k is represented by the sequence U 1 k corresponds to U k 's big bang. Different algorithms may compute the same universe. Some universes are finite (those whose programs cease producing outputs at some point), others are not. I don't know about ours. TM not important. The choice of the Turing machine is not important. This is due to the compiler theorem: for each universal Turing machine C there exists a constant prefix C 2 f "0","1",","g fl such that for all possible programs p, C's output in response to program C p is identical to T M 's output in response to p. The prefix C is the compiler that compiles programs for T M into equivalent programs for C. k denote the l-th (possibly empty) bitstring before the l-th comma. U l | [
68,
1779,
1780
] | Validation |
2,008 | 3 | Title: Self-Targeting Candidates for Metropolis-Hastings Algorithms
Abstract: The Metropolis-Hastings algorithm for estimating a distribution is based on choosing a candidate Markov chain and then accepting or rejecting moves of the candidate to produce a chain known to have as the invariant measure. The traditional methods use candidates essentially unconnected to . Based on diffusions for which is invariant, we develop for one-dimensional distributions a class of candidate distributions that "self-target" towards the high density areas of . These produce Metropolis-Hastings algorithms with convergence rates that appear to be considerably better than those known for the traditional candidate choices, such as random walk. In particular, for wide classes of these choices may effectively help reduce the "burn-in" problem. We illustrate this behaviour for examples with exponential and polynomial tails, and for a logistic regression model using a Gibbs sampling algorithm. | [
2022,
2153,
2219
] | Test |
2,009 | 5 | Title: The Predictability of Data Values
Abstract: Copyright 1997 IEEE. Published in the Proceedings of Micro-30, December 1-3, 1997 in Research Triangle Park, North Carolina. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions IEEE Service Center 445 Hoes Lane P.O. Box 1331 Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966. | [
2534
] | Test |
2,010 | 0 | Title: Learning in design: From Characterizing Dimensions to Working Systems
Abstract: The application of machine learning (ML) to solve practical problems is complex. Only recently, due to the increased promise of ML in solving real problems and the experienced difficulty of their use, has this issue started to attract attention. This difficulty arises from the complexity of learning problems and the large variety of available techniques. In order to understand this complexity and begin to overcome it, it is important to construct a characterization of learning situations. Building on previous work that dealt with the practical use of ML, a set of dimensions is developed, contrasted with another recent proposal, and illustrated with a project on the development of a decision-support system for marine propeller design. The general research opportunities that emerge from the development of the dimensions are discussed. Leading toward working systems, a simple model is presented for setting priorities in research and in selecting learning tasks within large projects. Central to the development of the concepts discussed in this paper is their use in future projects and the recording of their successes, limitations, and failures. | [
2447
] | Train |
2,011 | 6 | Title: An O(n log log n Learning Algorithm for DNF under the Uniform Distribution
Abstract: We show that a DNF with terms of size at most d can be approximated by a function with at most d O(d log1=") non zero Fourier coefficients such that the expected error squared, with respect to the uniform distribution, is at most ". This property is used to derive a learning algorithm for DNF, under the uniform distribution. The learning algorithm uses queries and learns, with respect to the uniform distribution, a DNF with terms of size at most d in time polynomial in n and d O(d log 1=") . The interesting implications are for the case when " is constant. In this case our algorithm learns a DNF with a polynomial number of terms in time n O(log log n) , and a DNF with terms of size at most O(log n= log log n) in polynomial time. | [
1835,
2182,
2633
] | Test |
2,012 | 6 | Title: Multivariate Decision Trees
Abstract: COINS Technical Report 92-82 December 1992 Abstract Multivariate decision trees overcome a representational limitation of univariate decision trees: univariate decision trees are restricted to splits of the instance space that are orthogonal to the feature's axis. This paper discusses the following issues for constructing multivariate decision trees: representing a multivariate test, including symbolic and numeric features, learning the coefficients of a multivariate test, selecting the features to include in a test, and pruning of multivariate decision trees. We present some new and review some well-known methods for forming multivariate decision trees. The methods are compared across a variety of learning tasks to assess each method's ability to find concise, accurate decision trees. The results demonstrate that some multivariate methods are more effective than others. In addition, the experiments confirm that allowing multivariate tests improves the accuracy of the resulting decision tree over univariate trees. | [
102,
378,
2135
] | Train |
2,013 | 2 | Title: Using the Stochastic Gradient Method to Fit Polychotomous Regression Models
Abstract: Technical Report No. 319 April 7, 1997 University of Washington Department of Statistics Seattle, Washington 98195-4322 Abstract Kooperberg, Bose, and Stone (1997) introduced polyclass, a methodology that uses adaptively selected linear splines and their tensor products to model conditional class probabilities. The authors attempted to develop a methodology that would work well on small and moderate size problems and would scale up to large problems. However, the version of polyclass that was developed for large problems was impractical in that it required two months of cpu time to apply it to a large data set. A modification to this methodology involving the use of the stochastic gradient (on-line) method in fitting polyclass models to given sets of basis functions is developed here that makes the methodology applicable to large data sets. In particular, it is successfully applied to a phoneme recognition problem involving 45 phonemes, 81 features, 150,000 cases in the training sample, 1000 basis functions, and 44,000 unknown parameters. Comparisons with neural networks are made both on the original problem and on a three-vowel subproblem. | [
74,
2382
] | Train |
2,014 | 4 | Title: Emergent Hierarchical Control Structures: Learning Reactive/Hierarchical Relationships in Reinforcement Environments
Abstract: The use of externally imposed hierarchical structures to reduce the complexity of learning control is common. However, it is acknowledged that learning the hierarchical structure itself is an important step towards more general (learning of many things as required) and less bounded (learning of a single thing as specified) learning. Presented in this paper is a reinforcement learning algorithm called Nested Q-learning that generates a hierarchical control structure in reinforcement learning domains. The emergent structure combined with learned bottom-up reactive reactions results in a reactive hierarchical control system. Effectively, the learned hierarchy decomposes what would otherwise be a monolithic evaluation function into many smaller evaluation functions that can be recombined without the loss of previously learned information. | [
562,
1828,
2018
] | Train |
2,015 | 6 | Title: On-Line Portfolio Selection Using Multiplicative Updates
Abstract: We present an on-line investment algorithm which achieves almost the same wealth as the best constant-rebalanced portfolio determined in hindsight from the actual market outcomes. The algorithm employs a multiplicative update rule derived using a framework introduced by Kivinen and Warmuth. Our algorithm is very simple to implement and requires only constant storage and computing time per stock in each trading period. We tested the performance of our algorithm on real stock data from the New York Stock Exchange accumulated during a 22-year period. On this data, our algorithm clearly outperforms the best single stock as well as Cover's universal portfolio selection algorithm. We also present results for the situation in which the investor has access to additional "side information." | [
2034,
2092,
2327
] | Train |
2,016 | 3 | Title: On the Semantics of Belief Revision Systems
Abstract: We consider belief revision operators that satisfy the Alchourron-Gardenfors-Makinson postulates, and present an epistemic logic in which, for any such revision operator, the result of a revision can be described by a sentence in the logic. In our logic, the fact that the agent's set of beliefs is is represented by the sentence O, where O is Levesque's `only know' operator. Intuitively, O is read as ` is all that is believed.' The fact that the agent believes is represented by the sentence B , read in the usual way as ` is believed'. The connective represents update as defined by Katsuno and Mendelzon. The revised beliefs are represented by the sentence O B . We show that for every revision operator that satisfies the AGM postulates, there is a model for our epistemic logic such that the beliefs implied by the sentence O B in this model correspond exactly to the sentences implied by the theory that results from revising by . This means that reasoning about changes in the agent's beliefs reduces to model checking of certain epistemic sentences. The negative result in the paper is that this type of formal account of revision cannot be extended to the situation where the agent is able to reason about its beliefs. A fully introspective agent cannot use our construction to reason about the results of its own revisions, on pain of triviality. | [
342,
467,
1800
] | Train |
2,017 | 3 | Title: 28 Learning Bayesian Networks Using Feature Selection
Abstract: This paper introduces a novel enhancement for learning Bayesian networks with a bias for small, high-predictive-accuracy networks. The new approach selects a subset of features that maximizes predictive accuracy prior to the network learning phase. We examine explicitly the effects of two aspects of the algorithm, feature selection and node ordering. Our approach generates networks that are computationally simpler to evaluate and display predictive accuracy comparable Bayesian networks are being increasingly recognized as an important representation for probabilistic reasoning. For many domains, the need to specify the probability distributions for a Bayesian network is considerable, and learning these probabilities from data using an algorithm like K2 [Cooper92] could alleviate such specification difficulties. We describe an extension to the Bayesian network learning approaches introduced in K2. Our goal is to construct networks that are simpler to evaluate but still have high predictive accuracy relative to networks that model all features. Rather than use all database features (or attributes) for constructing the network, we select a subset of features that maximize the predictive accuracy of the network. Then the learning process uses only the selected features as nodes in learning the Bayesian network. We examine explicitly the effects of two aspects of the algorithm: (a) feature selection, and (b) node ordering. Our experimental results verify that this approach generates networks that are compu-tationally simpler to evaluate and display predictive accuracy comparable to the predictive accuracy of Bayesian networks that model all features. Our results, similar to those observed by other studies of feature selection in learning [Caruana94, John94, Langley94a, Langley94b], demonstrate that feature selection provides comparable predictive accuracy using smaller networks. For example, by selecting as few as 18% of the features for the to that of Bayesian networks which model all attributes. | [
1479,
1582,
2677
] | Train |
2,018 | 4 | Title: Learning Hierarchical Control Structures for Multiple Tasks and Changing Environments
Abstract: While the need for hierarchies within control systems is apparent, it is also clear to many researchers that such hierarchies should be learned. Learning both the structure and the component behaviors is a difficult task. The benefit of learning the hierarchical structures of behaviors is that the decomposition of the control structure into smaller transportable chunks allows previously learned knowledge to be applied to new but related tasks. Presented in this paper are improvements to Nested Q-learning (NQL) that allow more realistic learning of control hierarchies in reinforcement environments. Also presented is a simulation of a simple robot performing a series of related tasks that is used to compare both hierarchical and non-hierarchal learning techniques. | [
562,
1828,
2014
] | Train |
2,019 | 2 | Title: Ensemble Training: Some Recent Experiments with Postal Zip Data
Abstract: Recent findings suggest that a classification scheme based on an ensemble of networks is an effective way to address overfitting. We study optimal methods for training an ensemble of networks. Some recent experiments on Postal Zip-code character data suggest that weight decay may not be an optimal method for controlling the variance of a classifier. | [
157,
2147
] | Train |
2,020 | 2 | Title: Variational Gaussian Process Classifiers
Abstract: | [
1857
] | Train |
2,021 | 2 | Title: Best-First Model Merging for Dynamic Learning and Recognition
Abstract: Best-first model merging is a general technique for dynamically choosing the structure of a neural or related architecture while avoiding overfitting. It is applicable to both learning and recognition tasks and often generalizes significantly better than fixed structures. We demonstrate the approach applied to the tasks of choosing radial basis functions for function learning, choosing local affine models for curve and constraint surface modelling, and choosing the structure of a balltree or bumptree to maximize efficiency of access. | [
87,
157,
2218,
2428
] | Test |
2,022 | 3 | Title: Geometric and Subgeometric Convergence of Diffusions with Given Stationary Distributions, and Their Discretizations
Abstract: We describe algorithms for estimating a given measure known up to a constant of proportionality, based on a large class of diffusions (extending the Langevin model) for which is invariant. We show that under weak conditions one can choose from this class in such a way that the diffusions converge at exponential rate to , and one can even ensure that convergence is independent of the starting point of the algorithm. When convergence is less than exponential we show that it is often polynomial at known rates. We then consider methods of discretizing the diffusion in time, and find methods which inherit the convergence rates of the continuous time process. These contrast with the behaviour of the naive or Euler discretization, which can behave badly even in simple cases. | [
2008,
2153,
2219
] | Test |
2,023 | 2 | Title: Classification of EEG Signals Using a Sparse Polynomial Builder
Abstract: Edward S. Orosz and Charles W. Anderson Technical Report CS-94-111 April 27, 1994 | [
2135
] | Test |
2,024 | 2 | Title: Analysis of Linsker's application of Hebbian rules to Linear Networks
Abstract: Linsker has reported the development of structured receptive fields in simulations using a Hebb-type synaptic plasticity rule in a feed-forward linear network. The synapses develop under dynamics determined by a matrix that is closely related to the covariance matrix of input cell activities. We analyse the dynamics of the learning rule in terms of the eigenvectors of this matrix. These eigenvectors represent independently evolving weight structures. Some general theorems are presented regarding the properties of these eigenvectors and their eigenvalues. For a general covariance matrix four principal parameter regimes are predicted. We concentrate on the gaussian covariances at layer B ! C of Linsker's network. Analytic and numerical solutions for the eigenvectors at this layer are presented. Three eigenvectors dominate the dynamics: a DC eigenvector, in which all synapses have the same sign; a bi-lobed, oriented eigenvector; and a circularly symmetric, centre-surround eigenvector. Analysis of the circumstances in which each of these vectors dominates yields an explanation of the emergence of centre-surround structures and symmetry-breaking bi-lobed structures. Criteria are developed estimating the boundary of the parameter regime in which centre-surround structures emerge. The application of our analysis to Linsker's higher layers, at which the covariance functions were oscillatory, is briefly discussed. | [
427,
737,
1778,
1932
] | Test |
2,025 | 3 | Title: WEAK CONVERGENCE AND OPTIMAL SCALING OF RANDOM WALK METROPOLIS ALGORITHMS
Abstract: This paper considers the problem of scaling the proposal distribution of a multidimensional random walk Metropolis algorithm, in order to maximize the efficiency of the algorithm. The main result is a weak convergence result as the dimension of a sequence of target densities, n, converges to 1. When the proposal variance is appropriately scaled according to n, the sequence of stochastic processes formed by the first component of each Markov chain, converge to the appropriate limiting Langevin diffusion process. The limiting diffusion approximation admits a straight-forward efficiency maximization problem, and the resulting asymptotically optimal policy is related to the asymptotic acceptance rate of proposed moves for the algorithm. The asymptotically optimal acceptance rate is 0.234 under quite general conditions. The main result is proved in the case where the target density has a symmetric product form. Extensions of the result are discussed. | [
2153,
2377,
2693
] | Train |
2,026 | 2 | Title: Learning overcomplete representations
Abstract: This paper considers the problem of scaling the proposal distribution of a multidimensional random walk Metropolis algorithm, in order to maximize the efficiency of the algorithm. The main result is a weak convergence result as the dimension of a sequence of target densities, n, converges to 1. When the proposal variance is appropriately scaled according to n, the sequence of stochastic processes formed by the first component of each Markov chain, converge to the appropriate limiting Langevin diffusion process. The limiting diffusion approximation admits a straight-forward efficiency maximization problem, and the resulting asymptotically optimal policy is related to the asymptotic acceptance rate of proposed moves for the algorithm. The asymptotically optimal acceptance rate is 0.234 under quite general conditions. The main result is proved in the case where the target density has a symmetric product form. Extensions of the result are discussed. | [
570,
576,
1922,
2552
] | Train |
2,027 | 4 | Title: Coordinating Reactive Behaviors keywords: reactive systems, planning and learning
Abstract: Combinating reactivity with planning has been proposed as a means of compensating for potentially slow response times of planners while still making progress toward long term goals. The demands of rapid response and the complexity of many environments make it difficult to decompose, tune and coordinate reactive behaviors while ensuring consistency. Neural networks can address the tuning problem, but are less useful for decomposition and coordination. We hypothesize that interacting reactions can be decomposed into separate behaviors resident in separate networks and that the interaction can be coordinated through the tuning mechanism and a higher level controller. To explore these issues, we have implemented a neural network architecture as the reactive component of a two layer control system for a simulated race car. By varying the architecture, we test whether decomposing reactivity into separate behaviors leads to superior overall performance, coordination and learning convergence. | [
465,
565,
636,
2409
] | Train |
2,028 | 6 | Title: Teaching a Smarter Learner
Abstract: We introduce a formal model of teaching in which the teacher is tailored to a particular learner, yet the teaching protocol is designed so that no collusion is possible. Not surprisingly, such a model remedies the non-intuitive aspects of other models in which the teacher must successfully teach any consistent learner. We prove that any class that can be exactly identified by a deterministic polynomial-time algorithm with access to a very rich set of example-based queries is teachable by a computationally unbounded teacher and a polynomial-time learner. In addition, we present other general results relating this model of teaching to various previous results. We also consider the problem of designing teacher/learner pairs in which both the teacher and learner are polynomial-time algorithms and describe teacher/learner pairs for the classes of 1-decision lists and Horn sentences. | [
308,
1003,
2653
] | Train |
2,029 | 2 | Title: A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers
Abstract: This paper explores some algorithms for automatic quantization of real-valued datasets using thermometer codes for pattern classification applications. Experimental results indicate that a relatively simple randomized thermometer code generation technique can result in quantized datasets that when used to train simple perceptrons, can yield generalization on test data that is substantially better than that obtained with their unquantized counterparts. | [
503,
1818,
1952,
2393
] | Train |
2,030 | 1 | Title: Using Modeling Knowledge to Guide Design Space Search
Abstract: Automated search of a space of candidate designs seems an attractive way to improve the traditional engineering design process. To make this approach work, however, the automated design system must include both knowledge of the modeling limitations of the method used to evaluate candidate designs and also an effective way to use this knowledge to influence the search process. We suggest that a productive approach is to include this knowledge by implementing a set of model constraint functions which measure how much each modeling assumptions is violated, and to influence the search by using the values of these model constraint functions as constraint inputs to a standard constrained nonlinear optimization numerical method. We test this idea in the domain of conceptual design of supersonic transport aircraft, and our experiments indicate that our model constraint communication strategy can decrease the cost of design space search by one or more orders of magnitude. | [
743,
744,
2077,
2128,
2130,
2131,
2316,
2659
] | Train |
2,031 | 6 | Title: TOWARDS CONCEPT FORMATION GROUNDED ON PERCEPTION AND ACTION OF A MOBILE ROBOT
Abstract: The recognition of objects and, hence, their descriptions must be grounded in the environment in terms of sensor data. We argue, why the concepts, used to classify perceived objects and used to perform actions on these objects, should integrate action-oriented perceptual features and perception-oriented action features. We present a grounded symbolic representation for these concepts. Moreover, the concepts should be learned. We show a logic-oriented approach to learning grounded concepts. | [
2171
] | Validation |
2,032 | 5 | Title: Learning Action-oriented Perceptual Features for Robot Navigation
Abstract: The recognition of objects and, hence, their descriptions must be grounded in the environment in terms of sensor data. We argue, why the concepts, used to classify perceived objects and used to perform actions on these objects, should integrate action-oriented perceptual features and perception-oriented action features. We present a grounded symbolic representation for these concepts. Moreover, the concepts should be learned. We show a logic-oriented approach to learning grounded concepts. | [
344,
2171
] | Train |
2,033 | 2 | Title: Improving RBF Networks by the Feature Selection Approach EUBAFES
Abstract: The curse of dimensionality is one of the severest problems concerning the application of RBF networks. The number of RBF nodes and therefore the number of training examples needed grows exponentially with the intrinsic dimensionality of the input space. One way to address this problem is the application of feature selection as a data preprocessing step. In this paper we propose a two-step approach for the determination of an optimal feature subset: First, all possible feature-subsets are reduced to those with best discrimination properties by the application of the fast and robust filter technique EUBAFES. Secondly we use a wrapper approach to judge, which of the pre-selected feature subsets leads to RBF networks with least complexity and best classification accuracy. Experiments are undertaken to show the improvement for RBF networks by our feature selection approach. | [
430,
2622
] | Train |
2,034 | 3 | Title: Update rules for parameter estimation in Bayesian networks
Abstract: This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [12]. We provide a unified framework for parameter estimation that encompasses both on-line learning, where the model is continuously adapted to new data cases as they arrive, and the more traditional batch learning, where a pre-accumulated set of samples is used in a one-time model selection process. In the batch case, our framework encompasses both the gradient projection algorithm [2, 3] and the EM algorithm [14] for Bayesian networks. The framework also leads to new on-line and batch parameter update schemes, including a parameterized version of EM. We provide both empirical and theoretical results indicating that parameterized EM allows faster convergence to the maximum likelihood parame ters than does standard EM. | [
453,
558,
577,
2015,
2327
] | Train |
2,035 | 0 | Title: Knowledge Compilation and Speedup Learning in Continuous Task Domains
Abstract: Many techniques for speedup learning and knowledge compilation focus on the learning and optimization of macro-operators or control rules in task domains that can be characterized using a problem-space search paradigm. However, such a characterization does not fit well the class of task domains in which the problem solver is required to perform in a continuous manner. For example, in many robotic domains, the problem solver is required to monitor real-valued perceptual inputs and vary its motor control parameters in a continuous, on-line manner to successfully accomplish its task. In such domains, discrete symbolic states and operators are difficult to define. To improve its performance in continuous problem domains, a problem solver must learn, modify, and use continuous operators that continuously map input sensory information to appropriate control outputs. Additionally, the problem solver must learn the contexts in which those continuous operators are applicable. We propose a learning method that can compile sensorimo-tor experiences into continuous operators, which can then be used to improve performance of the problem solver. The method speeds up the task performance as well as results in improvements in the quality of the resulting solutions. The method is implemented in a robotic navigation system, which is evaluated through extensive experimen tation. | [
858,
1084,
2303
] | Train |
2,036 | 6 | Title: Query, PACS and simple-PAC Learning
Abstract: We study a distribution dependent form of PAC learning that uses probability distributions related to Kolmogorov complexity. We relate the PACS model, defined by Denis, D'Halluin and Gilleron in [3], with the standard simple-PAC model and give a general technique that subsumes the results in [3] and [6]. | [
2696
] | Test |
2,037 | 0 | Title: Formalising the knowledge content of case memory systems
Abstract: Discussions of case-based reasoning often reflect an implicit assumption that a case memory system will become better informed, i.e. will increase in knowledge, as more cases are added to the case-base. This paper considers formalisations of this `knowledge content' which are a necessary preliminary to more rigourous analysis of the performance of case-based reasoning systems. In particular we are interested in modelling the learning aspects of case-based reasoning in order to study how the performance of a case-based reasoning system changes as it accumlates problem-solving experience. The current paper presents a `case-base semantics' which generalises recent formalisations of case-based classification. Within this framework, the paper explores various issues in assuring that these sematics are well-defined, and illustrates how the knowledge content of the case memory system can be seen to reside in both the chosen similarity measure and in the cases of the case-base. | [
288,
1584,
2151
] | Validation |
2,038 | 0 | Title: Knowledge Based Systems
Abstract: Technical Report No. 95/2 | [
985,
2692
] | Train |
2,039 | 1 | Title: A Case Study on Tuning of Genetic Algorithms by Using Performance Evaluation Based on Experimental Design
Abstract: This paper proposes four performance measures of a genetic algorithm (GA) which enable us to compare different GAs for an op timization problem and different choices of their parameters' values. The performance measures are defined in terms of observations in simulation, such as the frequency of optimal solutions, fitness values, the frequency of evolution leaps, and the number of generations needed to reach an optimal solution. We present a case study in which parameters of a GA for robot path planning was tuned and its performance was optimized through performance evaluation by using the measures. Especially, one of the performance measures is used to demonstrate the adaptivity of the GA for robot path planning. We also propose a process of systematic tuning based on techniques for the design of experiments. | [
163,
1060,
1890,
2254
] | Train |
2,040 | 6 | Title: On the Learnability and Usage of Acyclic Probabilistic Finite Automata
Abstract: We propose and analyze a distribution learning algorithm for a subclass of Acyclic Probabilistic Finite Automata (APFA). This subclass is characterized by a certain distinguishability property of the automata's states. Though hardness results are known for learning distributions generated by general APFAs, we prove that our algorithm can efficiently learn distributions generated by the subclass of APFAs we consider. In particular, we show that the KL-divergence between the distribution generated by the target source and the distribution generated by our hypothesis can be made arbitrarily small with high confidence in polynomial time. We present two applications of our algorithm. In the first, we show how to model cursively written letters. The resulting models are part of a complete cursive handwriting recognition system. In the second application we demonstrate how APFAs can be used to build multiple-pronunciation models for spoken words. We evaluate the APFA based pronunciation models on labeled speech data. The good performance (in terms of the log-likelihood obtained on test data) achieved by the APFAs and the little time needed for learning suggests that the learning algorithm of APFAs might be a powerful alternative to commonly used probabilistic models. | [
574,
672,
1006,
1924,
2004,
2360
] | Test |
2,041 | 2 | Title: Natural Language Grammatical Inference with Recurrent Neural Networks
Abstract: This paper examines the inductive inference of a complex grammar with neural networks specifically, the task considered is that of training a network to classify natural language sentences as grammatical or ungrammatical, thereby exhibiting the same kind of discriminatory power provided by the Principles and Parameters linguistic framework, or Government-and-Binding theory. Neural networks are trained, without the division into learned vs. innate components assumed by Chomsky, in an attempt to produce the same judgments as native speakers on sharply grammatical/ungrammatical data. How a recurrent neural network could possess linguistic capability, and the properties of various common recurrent neural network architectures are discussed. The problem exhibits training behavior which is often not present with smaller grammars, and training was initially difficult. However, after implementing several techniques aimed at improving the convergence of the gradient descent backpropagation-through- time training algorithm, significant learning was possible. It was found that certain architectures are better able to learn an appropriate grammar. The operation of the networks and their training is analyzed. Finally, the extraction of rules in the form of deterministic finite state automata is investigated. | [
1744
] | Train |
2,042 | 2 | Title: Fast Bounded Smooth Regression with Lazy Neural Trees
Abstract: We propose the lazy neural tree (LNT) as the appropriate architecture for the realization of smooth regression systems. The LNT is a hybrid of a decision tree and a neural network. From the neural network it inherits smoothness of the generated function, incremental adaptability, and conceptual simplicity. From the decision tree it inherits the topology and initial parameter setting as well as a very efficient sequential implementation that out-performs traditional neural network simulations by the order of magnitudes. The enormous speed is achieved by lazy evaluation. A further speed-up can be obtained by the application of a window-ing scheme if the region of interesting results is restricted. | [
378,
2428
] | Test |
2,043 | 2 | Title: Using Mixtures of Factor Analyzers for Segmentation and Pose Estimation Category: Visual Processing Preference: Oral
Abstract: To read a hand-written digit string, it is helpful to segment the image into separate digits. Bottom-up segmentation heuristics often fail when neighboring digits overlap substantially. We describe a system that has a stochastic generative model of each digit class and we show that this is the only knowledge required for segmentation. The system uses Gibbs sampling to construct a perceptual interpretation of a digit string and segmentation arises naturally from the "explaining away" effects that occur during Bayesian inference. By using conditional mixtures of factor analyzers, it is possible to extract an explicit, compact representation of the instantiation parameters that describe the pose of each digit. These instantiation parameters can then be used as the inputs to a higher level system that models the relationships between digits. The same technique could be used to model individual digits as redundancies between the instantiation parameters of their parts. | [
2270
] | Train |
2,044 | 2 | Title: Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,
Abstract: | [
80,
427,
1149,
2683
] | Train |
2,045 | 5 | Title: The Arguments of Newly Invented Predicates in ILP
Abstract: In this paper we investigate the problem of choosing arguments for a new predicate. We identify the relevant terms to be considered as arguments, and propose methods to choose among them based on propositional minimisation. | [
638,
2550
] | Train |
2,046 | 2 | Title: A Method for Identifying Splice Sites and Translational Start Sites in
Abstract: This paper describes a new method for determining the consensus sequences that signal the start of translation and the boundaries between exons and introns (donor and acceptor sites) in eukaryotic mRNA. The method takes into account the dependencies between adjacent bases, in contrast to the usual technique of considering each position independently. When coupled with a dynamic program to compute the most likely sequence, new consensus sequences emerge. The consensus sequence information is summarized in conditional probability matrices which, when used to locate signals in uncharacter-ized genomic DNA, have greater sensitivity and specificity than conventional matrices. Species-specific versions of these matrices are especially effective at distinguishing true and false sites. | [
268,
616,
2107
] | Test |
2,047 | 1 | Title: Evolutionary wanderlust: Sexual selection with directional mate preferences. Evolutionary wanderlust: Sexual selection with directional mate preferences
Abstract: In the pantheon of evolutionary forces, the optimizing Apollonian powers of natural selection are generally assumed to dominate the dark Dionysian dynamics of sexual selection. But this need not be the case, particularly with a class of selective mating mechanisms called `directional mate preferences' (Kirkpatrick, 1987). In previous simulation research, we showed that nondirectional assortative mating preferences could cause populations to spontaneously split apart into separate species (Todd & Miller, 1991). In this paper, we show that directional mate preferences can cause populations to wander capriciously through phenotype space, under a strange form of runaway sexual selection, with or without the influence of natural selection pressures. When directional mate preferences are free to evolve, they do not always evolve to point in the direction of natural-selective peaks. Sexual selection can thus take on a life of its own, such that mate preferences within a species become a distinct and important part of the environment to which the species' phenotypes adapt. These results suggest a broader conception of `adaptive behavior', in which attracting potential mates becomes as important as finding food and avoiding predators. We present a framework for simulating a wide range of directional and non-directional mate preferences, and discuss some practical and scientific applications of simu lating sexual selection. | [
2111
] | Train |
2,048 | 0 | Title: Technical Diagnosis: Fallexperte-D of further knowledge sources (domain knowledge, common knowledge) is investigated in the
Abstract: Case based reasoning (CBR) uses the knowledge from former experiences ("known cases"). Since special knowledge of an expert is mainly subject to his experiences, the CBR techniques are a good base for the development of expert systems. We investigate the problem for technical diagnosis. Diagnosis is not considered as a classification task, but as a process to be guided by computer assisted experience. This corresponds to the flexible "case completion" approach. Flexibility is also needed for the expert view with predominant interest in the unexpected, unpredictible cases. | [
1854
] | Train |
2,049 | 2 | Title: Learning Feature-based Semantics with Simple Recurrent Networks
Abstract: The paper investigates the possibilities for using simple recurrent networks as transducers which map sequential natural language input into non-sequential feature-based semantics. The networks perform well on sentences containing a single main predicate (encoded by transitive verbs or prepositions) applied to multiple-feature objects (encoded as noun-phrases with adjectival modifiers), and shows robustness against ungrammatical inputs. A second set of experiments deals with sentences containing embedded structures. Here the network is able to process multiple levels of sentence-final embeddings but only one level of center-embedding. This turns out to be a consequence of the network's inability to retain information that is not reflected in the outputs over intermediate phases of processing. Two extensions to Elman's [9] original recurrent network architecture are introduced. | [
2218,
2306,
2410
] | Train |
2,050 | 2 | Title: TABLE DES MATI ERES 1 Apprentissage et approximation les techniques de regularisation 3 1.1 Introduction
Abstract: The paper investigates the possibilities for using simple recurrent networks as transducers which map sequential natural language input into non-sequential feature-based semantics. The networks perform well on sentences containing a single main predicate (encoded by transitive verbs or prepositions) applied to multiple-feature objects (encoded as noun-phrases with adjectival modifiers), and shows robustness against ungrammatical inputs. A second set of experiments deals with sentences containing embedded structures. Here the network is able to process multiple levels of sentence-final embeddings but only one level of center-embedding. This turns out to be a consequence of the network's inability to retain information that is not reflected in the outputs over intermediate phases of processing. Two extensions to Elman's [9] original recurrent network architecture are introduced. | [
611,
2378
] | Validation |
2,051 | 4 | Title: Emergent Control and Planning in an Autonomous Vehicle
Abstract: We use a connectionist network trained with reinforcement to control both an autonomous robot vehicle and a simulated robot. We show that given appropriate sensory data and architectural structure, a network can learn to control the robot for a simple navigation problem. We then investigate a more complex goal-based problem and examine the plan-like behavior that emerges. | [
1969
] | Test |
2,052 | 0 | Title: Applying Case-Based Reasoning to Control in Robotics
Abstract: The proposed architecture is experimentally evaluated on two real world domains and the results are compared to other machine learning algorithms applied to the same problem. | [
1483,
2062
] | Train |
2,053 | 6 | Title: On the Complexity of Learning from Drifting Distributions
Abstract: The proposed architecture is experimentally evaluated on two real world domains and the results are compared to other machine learning algorithms applied to the same problem. | [
109,
488,
2054,
2685,
2690
] | Validation |
2,054 | 6 | Title: Tracking Drifting Concepts By Minimizing Disagreements
Abstract: In this paper we consider the problem of tracking a subset of a domain (called the target) which changes gradually over time. A single (unknown) probability distribution over the domain is used to generate random examples for the learning algorithm and measure the speed at which the target changes. Clearly, the more rapidly the target moves, the harder it is for the algorithm to maintain a good approximation of the target. Therefore we evaluate algorithms based on how much movement of the target can be tolerated between examples while predicting with accuracy *. Furthermore, the complexity of the class H of possible targets, as measured by d, its VC-dimension, also effects the difficulty of tracking the target concept. We show that if the problem of minimizing the number of disagreements with a sample from among concepts in a class H can be approximated to within a factor k, then there is a simple tracking algorithm for H which can achieve a probability * of making a mistake if the target movement rate is at most a constant times * 2 =(k(d + k) ln 1 * ), where d is the Vapnik-Chervonenkis dimension of H. Also, we show that if H is properly PAC-learnable, then there is an efficient (randomized) algorithm that with high probability approximately minimizes disagreements to within a factor of 7d + 1, yielding an efficient tracking algorithm for H which tolerates drift rates up to a constant times * 2 =(d 2 ln 1 In addition, we prove complementary results for the classes of halfspaces and axis-aligned hy- perrectangles showing that the maximum rate of drift that any algorithm (even with unlimited computational power) can tolerate is a constant times * 2 =d. | [
109,
591,
640,
2053,
2685
] | Train |
2,055 | 1 | Title: 1 FEATURE SUBSET SELECTION USING A GENETIC ALGORITHM time needed for learning a sufficiently accurate
Abstract: Practical pattern classification and knowledge discovery problems require selection of a subset of attributes or features (from a much larger set) to represent the patterns to be classified. This is due to the fact that the performance of the classifier (usually induced by some learning algorithm) and the cost of classification are sensitive to the choice of the features used to construct the classifier. Exhaustive evaluation of possible feature subsets is usually infeasible in practice because of the large amount of computational effort required. Genetic algorithms, which belong to a class of randomized heuristic search techniques, offer an attractive approach to find near-optimal solutions to such optimization problems. This paper presents an approach to feature subset selection using a genetic algorithm. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy and cost of classification into the feature selection process and to find feature subsets that perform well for particular choices of the inductive learning algorithm used to construct the pattern classifier. Our experiments with several benchmark real-world pattern classification problems demonstrate the feasibility of this approach to feature subset selection in the automated Many practical pattern classification tasks (e.g., medical diagnosis) require learning of an appropriate classification function that assigns a given input pattern (typically represented using a vector of attribute or feature values) to one of a finite set of classes. The choice of features, attributes, or measurements used to represent patterns that are presented to a classifier affect (among other things): The accuracy of the classification function that can be learned using an inductive learning algorithm (e.g., a decision tree induction algorithm or a neural network learning algorithm): The features used to describe the patterns implicitly define a pattern language. If the language is not expressive enough, it would fail to capture the information that is necessary for classification and hence regardless of the learning algorithm used, the accuracy of the classification function learned would be limited by this lack of information. design of neural networks for pattern classification and knowledge discovery. | [
2352
] | Train |
2,056 | 2 | Title: PREDICTION WITH GAUSSIAN PROCESSES: FROM LINEAR REGRESSION TO LINEAR PREDICTION AND BEYOND To appear in
Abstract: The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads in to a more general discussion of Gaussian processes in section 4. Section 5 deals with further issues, including hierarchical modelling and the setting of the parameters that control the Gaussian process, the covariance functions for neural network models and the use of Gaussian processes in classification problems. | [
271,
2095
] | Validation |
2,057 | 0 | Title: Chunking in soar: The anatomy of a general learn ing mechanism. Machine Learning, 1(1). Learning
Abstract: gers University. Also appears as tech. report ML- TR-7. Minton, S. (1988). Quantitative results concerning the utility of explanation-based learning. In Proceedings of National Conference on Artificial Intelli gence, pages 564-569. St. Paul, MN. | [
790,
1510,
2215,
2465,
2695
] | Train |
2,058 | 1 | Title: Challenges in Evolving Controllers for Physical Robots
Abstract: This paper discusses the feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. We overview the state of the art in the field, describe some of the main approaches, discuss the key challenges, unanswered problems, and some promising directions. | [
755,
757,
2232
] | Train |
2,059 | 6 | Title: Challenges in Evolving Controllers for Physical Robots
Abstract: General convergence results for linear discriminant updates Abstract The problem of learning linear discriminant concepts can be solved by various mistake-driven update procedures, including the Winnow family of algorithms and the well-known Perceptron algorithm. In this paper we define the general class of quasi-additive algorithms, which includes Perceptron and Winnow as special cases. We give a single proof of convergence that covers much of this class, including both Perceptron and Winnow but also many novel algorithms. Our proof introduces a generic measure of progress that seems to capture much of when and how these algorithms converge. Using this measure, we develop a simple general technique for proving mistake bounds, which we apply to the new algorithms as well as existing algorithms. When applied to known algorithms, our technique automatically produces close variants of existing proofs (and we generally obtain the known bounds, to within constants) thus showing, in a certain sense, that these seem ingly diverse results are fundamentally isomorphic. | [
453,
2651
] | Test |
2,060 | 0 | Title: A Similarity-Based Retrieval Tool for Software Repositories
Abstract: In this paper we present a prototype of a flexible similarity-based retrieval system. Its flexibility is supported by allowing for an imprecisely specified query. Moreover, our algorithm allows for assessing if the retrieved items are relevant in the initial context, specified in the query. The presented system can be used as a supporting tool for a software repository. We also discuss system evaluation with concerns on usefulness, scalability, applicability and comparability. Evaluation of the T A3 system on three domains gives us encouraging results and an integration of TA3 into a real software repository as a retrieval tool is ongoing. | [
857,
1483,
2062
] | Train |
2,061 | 0 | Title: Inductive Learning and Case-Based Reasoning
Abstract: This paper describes an application of an inductive learning techniques to case-based reasoning. We introduce two main forms of induction, define case-based reasoning and present a combination of both. The evaluation of the proposed system, called TA3, is carried out on a classification task, namely character recognition. We show how inductive knowledge improves knowledge representation and in turn flexibility of the system, its performance (in terms of classification accuracy) and its scalability. | [
96,
215,
2062
] | Train |
2,062 | 0 | Title: A Case-Based Reasoning Approach
Abstract: The AAAI Fall Symposium; Flexible Computation in Intelligent Systems: Results, Issues, and Opportunities. Nov. 9-11, 1996, Cambridge, MA Abstract This paper presents a case-based reasoning system TA3. We address the flexibility of the case-based reasoning process, namely flexible retrieval of relevant experiences, by using a novel similarity assessment theory. To exemplify the advantages of such an approach, we have experimentally evaluated the system and compared its performance to the performance of non-flexible version of TA3 and to other machine learning algorithms on several domains. | [
2052,
2060,
2061,
2066
] | Train |
2,063 | 3 | Title: Planning Medical Therapy Using Partially Observable Markov Decision Processes.
Abstract: Diagnosis of a disease and its treatment are not separate, one-shot activities. Instead they are very often dependent and interleaved over time, mostly due to uncertainty about the underlying disease, uncertainty associated with the response of a patient to the treatment and varying cost of different treatment and diagnostic (investigative) procedures. The framework particularly suitable for modeling such a complex therapy decision process is Partially observable Markov decision process (POMDP). Unfortunately the problem of finding the optimal therapy within the standard POMDP framework is also computationally very costly. In this paper we investigate various structural extensions of the standard POMDP framework and approximation methods which allow us to simplify model construction process for larger therapy problems and to solve them faster. A therapy problem we target specifically is the management of patients with ischemic heart disease. | [
2494
] | Train |
2,064 | 3 | Title: A Market Framework for Pooling Opinions
Abstract: Consider a group of Bayesians, each with a subjective probability distribution over a set of uncertain events. An opinion pool derives a single consensus distribution over the events, representative of the group as a whole. Several pooling functions have been proposed, each sensible under particular assumptions or measures. Many researchers over many years have failed to form a consensus on which method is best. We propose a market-based pooling procedure, and analyze its properties. Participants bet on securities, each paying off contingent on an uncertain event, so as to maximize their own expected utilities. The consensus probability of each event is defined as the corresponding security's equilibrium price. The market framework provides explicit monetary incentives for participation and honesty, and allows agents to maintain individual rationality and limited privacy. "No arbitrage" arguments ensure that the equilibrium prices form legal probabilities. We show that, when events are disjoint and all participants have exponential utility for money, the market derives the same result as the logarithmic opinion pool; similarly, logarithmic utility for money yields the linear opinion pool. In both cases, we prove that the group's behavior is, to an outside observer, indistinguishable from that of a rational individual, whose beliefs equal the equilibrium prices. | [
1777,
1802
] | Train |
2,065 | 1 | Title: Performance Enhanced Genetic Programming
Abstract: Genetic Programming is increasing in popularity as the basis for a wide range of learning algorithms. However, the technique has to date only been successfully applied to modest tasks because of the performance overheads of evolving a large number of data structures, many of which do not correspond to a valid program. We address this problem directly and demonstrate how the evolutionary process can be achieved with much greater efficiency through the use of a formally-based representation and strong typing. We report initial experimental results which demonstrate that our technique exhibits significantly better performance than previous work. | [
1985,
2086
] | Train |
2,066 | 6 | Title: On the Informativeness of the DNA Promoter Sequences Domain Theory
Abstract: The DNA promoter sequences domain theory and database have become popular for testing systems that integrate empirical and analytical learning. This note reports a simple change and reinterpretation of the domain theory in terms of M-of-N concepts, involving no learning, that results in an accuracy of 93.4% on the 106 items of the database. Moreover, an exhaustive search of the space of M-of-N domain theory interpretations indicates that the expected accuracy of a randomly chosen interpretation is 76.5%, and that a maximum accuracy of 97.2% is achieved in 12 cases. This demonstrates the informativeness of the domain theory, without the complications of understanding the interactions between various learning algorithms and the theory. In addition, our results help characterize the difficulty of learning using the DNA promoters theory. | [
159,
985,
2062,
2674
] | Validation |
2,067 | 6 | Title: Classification by Pairwise Coupling
Abstract: We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together. The coupling model is similar to the Bradley-Terry method for paired comparisons. We study the nature of the class probability estimates that arise, and examine the performance of the procedure in real and simulated datasets. Classifiers used include linear discriminants, nearest neighbors, and the support vector machine. | [
1792
] | Validation |
2,068 | 2 | Title: Rearrangement of receptive field topography after intracortical and peripheral stimulation: The role of plasticity in
Abstract: Intracortical microstimulation (ICMS) of a single site in the somatosensory cortex of rats and monkeys for 2-6 hours produces a large increase in the number of neurons responsive to the skin region corresponding to the ICMS-site receptive field (RF), with very little effect on the position and size of the ICMS-site RF, and the response evoked at the ICMS site by tactile stimulation (Recanzone et al., 1992b). Large changes in RF topography are observed following several weeks of repetitive stimulation of a restricted skin region in monkeys (Jenkins et al., 1990; Recanzone et al., 1992acde). Repetitive stimulation of a localized skin region in monkeys produced by training the monkeys in a tactile frequency discrimination task improves their performance (Recanzone et al., 1992a). It has been suggested that these changes in RF topography are caused by competitive learning in excitatory pathways (Grajski & Merzenich, 1990; Jenkins et al., 1990; Recanzone et al., 1992abcde). ICMS almost simultaneously excites excitatory and inhibitory terminals and excitatory and inhibitory cortical neurons within a few microns of the stimulating electrode. Thus, this paper investigates the implications of the possibility that lateral inhibitory pathways too may undergo synaptic plasticity during ICMS. Lateral inhibitory pathways may also undergo synaptic plasticity in adult animals during peripheral conditioning. The "EXIN" (afferent excitatory and lateral inhibitory) synaptic plasticity rules | [
355,
2228
] | Validation |
2,069 | 3 | Title: A Note on Testing Exogeneity of Instrumental Variables (DRAFT PAPER)
Abstract: Intracortical microstimulation (ICMS) of a single site in the somatosensory cortex of rats and monkeys for 2-6 hours produces a large increase in the number of neurons responsive to the skin region corresponding to the ICMS-site receptive field (RF), with very little effect on the position and size of the ICMS-site RF, and the response evoked at the ICMS site by tactile stimulation (Recanzone et al., 1992b). Large changes in RF topography are observed following several weeks of repetitive stimulation of a restricted skin region in monkeys (Jenkins et al., 1990; Recanzone et al., 1992acde). Repetitive stimulation of a localized skin region in monkeys produced by training the monkeys in a tactile frequency discrimination task improves their performance (Recanzone et al., 1992a). It has been suggested that these changes in RF topography are caused by competitive learning in excitatory pathways (Grajski & Merzenich, 1990; Jenkins et al., 1990; Recanzone et al., 1992abcde). ICMS almost simultaneously excites excitatory and inhibitory terminals and excitatory and inhibitory cortical neurons within a few microns of the stimulating electrode. Thus, this paper investigates the implications of the possibility that lateral inhibitory pathways too may undergo synaptic plasticity during ICMS. Lateral inhibitory pathways may also undergo synaptic plasticity in adult animals during peripheral conditioning. The "EXIN" (afferent excitatory and lateral inhibitory) synaptic plasticity rules | [
260,
2434
] | Validation |
2,070 | 5 | Title: A Partial Memory Incremental Learning Methodology And Its Application To Computer Intrusion Detection
Abstract: Intracortical microstimulation (ICMS) of a single site in the somatosensory cortex of rats and monkeys for 2-6 hours produces a large increase in the number of neurons responsive to the skin region corresponding to the ICMS-site receptive field (RF), with very little effect on the position and size of the ICMS-site RF, and the response evoked at the ICMS site by tactile stimulation (Recanzone et al., 1992b). Large changes in RF topography are observed following several weeks of repetitive stimulation of a restricted skin region in monkeys (Jenkins et al., 1990; Recanzone et al., 1992acde). Repetitive stimulation of a localized skin region in monkeys produced by training the monkeys in a tactile frequency discrimination task improves their performance (Recanzone et al., 1992a). It has been suggested that these changes in RF topography are caused by competitive learning in excitatory pathways (Grajski & Merzenich, 1990; Jenkins et al., 1990; Recanzone et al., 1992abcde). ICMS almost simultaneously excites excitatory and inhibitory terminals and excitatory and inhibitory cortical neurons within a few microns of the stimulating electrode. Thus, this paper investigates the implications of the possibility that lateral inhibitory pathways too may undergo synaptic plasticity during ICMS. Lateral inhibitory pathways may also undergo synaptic plasticity in adult animals during peripheral conditioning. The "EXIN" (afferent excitatory and lateral inhibitory) synaptic plasticity rules | [
2602,
2640
] | Validation |
2,071 | 0 | Title: LINNEO A Classification Methodology for Ill-structured Domains
Abstract: In this work we present a classification methodology (LINNEO + ) to discover concepts from ill-structured domains and to organize hierarchies with them. In order to achieve this aim LINNEO + uses conceptual learning techniques and classification. The final target is to build knowledge bases after expert validation. Some techniques for the improvement of the results in the classification step are used, like biasing using partial expert knowledge (classification rules or causal and structural dependencies between attributes) or delayed cluster assignation of objects. Also some comparisons with a few well-known systems are shown. | [
1809
] | Test |
2,072 | 2 | Title: Data Mining for Association Rules with Unsupervised Neural Networks
Abstract: results for Gaussian mixture models and factor analysis are discussed. | [
36,
667,
2227
] | Train |
2,073 | 6 | Title: Classification Using -Machines and Constructive Function Approximation
Abstract: The classification algorithm CLEF combines a version of a linear machine known as a - machine with a non-linear function approxima-tor that constructs its own features. The algorithm finds non-linear decision boundaries by constructing features that are needed to learn the necessary discriminant functions. The CLEF algorithm is proven to separate all consistently labelled training instances, even when they are not linearly separable in the input variables. The algorithm is illustrated on a variety of tasks, showing an improvement over C4.5, a state-of-art de cision tree learning algorithm. | [
1818
] | Train |
2,074 | 0 | Title: The Management of Context-Sensitive Features: A Review of Strategies
Abstract: In this paper, we review five heuristic strategies for handling context-sensitive features in supervised machine learning from examples. We discuss two methods for recovering lost (implicit) contextual information. We mention some evidence that hybrid strategies can have a synergetic effect. We then show how the work of several machine learning researchers fits into this framework. While we do not claim that these strategies exhaust the possibilities, it appears that the framework includes all of the techniques that can be found in the published literature on context sensitive learning. | [
1636,
1647,
2607,
2615
] | Train |
2,075 | 0 | Title: Case Retrieval Nets: Foundations, Properties,
Abstract: Implementation, and Results | [
1855
] | Train |
2,076 | 3 | Title: Automated Discovery of Linear Feedback Models 1
Abstract: Implementation, and Results | [
211,
772,
1324,
1527,
2559
] | Test |
2,077 | 1 | Title: An Adaptive Penalty Approach for Constrained Genetic-Algorithm Optimization
Abstract: In this paper we describe a new adaptive penalty approach for handling constraints in genetic algorithm optimization problems. The idea is to start with a relatively small penalty coefficient and then increase it or decrease it on demand as the optimization progresses. Empirical results in several engineering design domains demonstrate the merit of the proposed approach. | [
163,
743,
744,
2030
] | Validation |
2,078 | 3 | Title: Structured Reachability Analysis for Markov Decision Processes
Abstract: Recent research in decision theoretic planning has focussed on making the solution of Markov decision processes (MDPs) more feasible. We develop a family of algorithms for structured reachability analysis of MDPs that are suitable when an initial state (or set of states) is known. Using compact, structured representations of MDPs (e.g., Bayesian networks), our methods, which vary in the tradeoff between complexity and accuracy, produce structured descriptions of (estimated) reachable states that can be used to eliminate variables or variable values from the problem description, reducing the size of the MDP and making it easier to solve. One contribution of our work is the extension of ideas from GRAPHPLAN to deal with the distributed nature of action representations typically embodied within Bayes nets and the problem of correlated action effects. We also demonstrate that our algorithm can be made more complete by using k-ary constraints instead of binary constraints. Another contribution is the illustration of how the compact representation of reachability constraints can be exploited by several existing (exact and approximate) abstraction algorithms for MDPs. | [
295,
1459,
1983,
2406,
2474
] | Validation |
2,079 | 5 | Title: Extraction of Meta-Knowledge to Restrict the Hypothesis Space for ILP Systems incorporating them in FOIL.
Abstract: Many ILP systems, such as GOLEM, FOIL, and MIS, take advantage of user supplied meta-knowledge to restrict the hypothesis space. This meta-knowledge can be in the form of type information about arguments in the predicate being learned, or it can be information about whether a certain argument in the predicate is functionally dependent on the other arguments (supplied as mode information). This meta knowledge is explicitly supplied to an ILP system in addition to the data. The present paper argues that in many cases the meta knowledge can be extracted directly from the raw data. Three algorithms are presented that learn type, mode, and symmetric meta-knowledge from data. These algorithms can be incorporated in existing ILP systems in the form of a preprocessor that obviates the need for a user to explicitly provide this information. In many cases, the algorithms can extract meta- knowledge that the user is either unaware of, but which information can be used by the ILP system to restrict the hypothesis space. | [
1428,
2609
] | Train |
2,080 | 6 | Title: Learning from positive data
Abstract: Gold showed in 1967 that not even regular grammars can be exactly identified from positive examples alone. Since it is known that children learn natural grammars almost exclusively from positives examples, Gold's result has been used as a theoretical support for Chomsky's theory of innate human linguistic abilities. In this paper new results are presented which show that within a Bayesian framework not only grammars, but also logic programs are learnable with arbitrarily low expected error from positive examples only. In addition, we show that the upper bound for expected error of a learner which maximises the Bayes' posterior probability when learning from positive examples is within a small additive term of one which does the same from a mixture of positive and negative examples. An Inductive Logic Programming implementation is described which avoids the pitfalls of greedy search by global optimisation of this function during the local construction of individual clauses of the hypothesis. Results of testing this implementation on artificially-generated data-sets are reported. These results are in agreement with the theoretical predictions. | [
1290,
2329,
2609
] | Test |
2,081 | 3 | Title: DE-NOISING BY reconstruction f n is defined in the wavelet domain by translating all the
Abstract: p n. We prove two results about that estimator. [Smooth]: With high probability ^ f fl n is at least as smooth as f , in any of a wide variety of smoothness measures. [Adapt]: The estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. Our proof of these results develops new facts about abstract statistical inference and its connection with Acknowledgements. These results were described at the Symposium on Wavelet Theory, held in connection with the Shanks Lectures at Van-derbilt University, April 3-4 1992. The author would like to thank Professor L.L. Schumaker for hospitality at the conference, and R.A. DeVore, Iain Johnstone, Gerard Kerkyacharian, Bradley Lucier, A.S. Nemirovskii, Ingram Olkin, and Dominique Picard for interesting discussions and correspondence on related topics. The author is also at the University of California, Berkeley | [
1910,
2159,
2366
] | Test |
2,082 | 1 | Title: %A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing
Abstract: | [
1775,
1793,
1795,
2178,
2545
] | Train |
2,083 | 2 | Title: TREE CONTRACTIONS AND EVOLUTIONARY TREES
Abstract: An evolutionary tree is a rooted tree where each internal vertex has at least two children and where the leaves are labeled with distinct symbols representing species. Evolutionary trees are useful for modeling the evolutionary history of species. An agreement subtree of two evolutionary trees is an evolutionary tree which is also a topological subtree of the two given trees. We give an algorithm to determine the largest possible number of leaves in any agreement subtree of two trees T 1 and T 2 with n leaves each. If the maximum degree d of these trees is bounded by a constant, the time complexity is O(n log 2 n) and is within a log n factor of optimal. For general d, this algorithm runs in O(nd 2 log d log 2 n) time or alternately in O(nd p d log 3 n) time. | [
299,
1827,
2511
] | Train |
2,084 | 2 | Title: Synthesize, Optimize, Analyze, Repeat (SOAR): Application of Neural Network Tools to ECG Patient Monitoring
Abstract: Results are reported from the application of tools for synthesizing, optimizing and analyzing neural networks to an ECG Patient Monitoring task. A neural network was synthesized from a rule-based classifier and optimized over a set of normal and abnormal heartbeats. The classification error rate on a separate and larger test set was reduced by a factor of 2. Sensitivity analysis of the synthesized and optimized networks revealed informative differences. Analysis of the weights and unit activations of the optimized network enabled a reduction in size of the network by a factor of 40% without loss of accuracy. | [
2615
] | Test |
2,085 | 2 | Title: Modeling dynamic receptive field changes produced by intracortical microstimulation
Abstract: Intracortical microstimulation (ICMS) of a localized site in the somatosensory cortex of rats and monkeys for 2-6 hours produces a large increase in the cortical representation of the skin region represented by the ICMS-site neurons before ICMS, with very little effect on the ICMS-site neuron's RF location, RF size, and responsiveness (Recanzone et al., 1992). The "EXIN" (afferent excitatory and lateral inhibitory) learning rules (Marshall, 1995) are used to model RF changes during ICMS. The EXIN model produces reorganization of RF topography similar to that observed experimentally. The possible role of inhibitory learning in producing the effects of ICMS is studied by simulating the EXIN model with only lateral inhibitory learning. The model also produces an increase in the cortical representation of the skin region represented by the ICMS-site RF. ICMS is compared to artificial scotoma conditioning (Pettet & Gilbert, 1992) and retinal lesions (Darian-Smith & Gilbert, 1995), and it is suggested that lateral inhibitory learning may be a general principle of cortical plasticity. | [
1093,
1094,
2228
] | Train |
2,086 | 1 | Title: ABSTRACT In general, the machine learning process can be accelerated through the use of additional
Abstract: Intracortical microstimulation (ICMS) of a localized site in the somatosensory cortex of rats and monkeys for 2-6 hours produces a large increase in the cortical representation of the skin region represented by the ICMS-site neurons before ICMS, with very little effect on the ICMS-site neuron's RF location, RF size, and responsiveness (Recanzone et al., 1992). The "EXIN" (afferent excitatory and lateral inhibitory) learning rules (Marshall, 1995) are used to model RF changes during ICMS. The EXIN model produces reorganization of RF topography similar to that observed experimentally. The possible role of inhibitory learning in producing the effects of ICMS is studied by simulating the EXIN model with only lateral inhibitory learning. The model also produces an increase in the cortical representation of the skin region represented by the ICMS-site RF. ICMS is compared to artificial scotoma conditioning (Pettet & Gilbert, 1992) and retinal lesions (Darian-Smith & Gilbert, 1995), and it is suggested that lateral inhibitory learning may be a general principle of cortical plasticity. | [
1231,
2065
] | Train |
2,087 | 1 | Title: Price's Theorem and the MAX Problem
Abstract: We present a detailed analysis of the evolution of GP populations using the problem of finding a program which returns the maximum possible value for a given terminal and function set and a depth limit on the program tree (known as the MAX problem). We confirm the basic message of [ Gathercole and Ross, 1996 ] that crossover together with program size restrictions can be responsible for premature convergence to a sub-optimal solution. We show that this can happen even when the population retains a high level of variety and show that in many cases evolution from the sub-optimal solution to the solution is possible if sufficient time is allowed. In both cases theoretical models are presented and compared with actual runs. Experimental evidence is presented that Price's Covariance and Selection Theorem can be applied to GP populations and the practical effect of program size restrictions are noted. Finally we show that covariance between gene frequency and fitness in the first few generations can be used to predict the course of GP runs. | [
1257,
1911,
2175,
2261
] | Train |
2,088 | 3 | Title: A Probabilistic Calculus of Actions
Abstract: We present a symbolic machinery that admits both probabilistic and causal information about a given domain, and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of conditioning operators: ordinary Bayes conditioning, P (yjX = x), which represents the observation X = x, and causal conditioning, P (yjdo(X = x)), read: the probability of Y = y conditioned on holding X constant (at x) by deliberate action. Given a mixture of such observational and causal sentences, together with the topology of the causal graph, the calculus derives new conditional probabilities of both types, thus enabling one to quantify the effects of actions and observations. | [
248,
776,
1527,
2167,
2524,
2525
] | Train |
2,089 | 1 | Title: A Cooperative Coevolutionary Approach to Function Optimization
Abstract: A general model for the coevolution of cooperating species is presented. This model is instantiated and tested in the domain of function optimization, and compared with a traditional GA-based function optimizer. The results are encouraging in two respects. They suggest ways in which the performance of GA and other EA-based optimizers can be improved, and they suggest a new approach to evolving complex structures such as neural networks and rule sets. | [
357,
714,
1117,
1261,
1530,
1603,
1965
] | Test |
2,090 | 2 | Title: Is Learning The n-th Thing Any Easier Than Learning The First?
Abstract: This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks. | [
1260,
2530
] | Train |
2,091 | 0 | Title: The Utility of Knowledge in Inductive Learning Running Head: Knowledge in Inductive Learning
Abstract: This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks. | [
303,
585,
1539,
1944,
2438,
2617
] | Test |
2,092 | 6 | Title: Universal Portfolios With and Without Transaction Costs
Abstract: A constant rebalanced portfolio is an investment strategy which keeps the same distribution of wealth among a set of stocks from period to period. Recently there has been work on on-line investment strategies that are competitive with the best constant rebalanced portfolio determined in hindsight (Cover, 1991; Helmbold et al., 1996; Cover and Ordentlich, 1996a; Cover and Ordentlich, 1996b; Ordentlich and Cover, 1996; Cover, 1996). For the universal algorithm of Cover (Cover, 1991), we provide a simple analysis which naturally extends to the case of a fixed percentage transaction cost (commission), answering a question raised in (Cover, 1991; Helmbold et al., 1996; Cover and Ordentlich, 1996a; Cover and Ordentlich, 1996b; Ordentlich and Cover, 1996; Cover, 1996). In addition, we present a simple randomized implementation that is significantly faster in practice. We conclude by explaining how these algorithms can be applied to other problems, such as combining the predictions of statistical language models, where the resulting guarantees are more striking. | [
453,
2015
] | Train |
2,093 | 2 | Title: Locally Connected Recurrent Networks
Abstract: Lai-Wan CHAN and Evan Fung-Yu YOUNG Computer Science Department, The Chinese University of Hong Kong New Territories, Hong Kong Email : lwchan@cs.cuhk.hk Technical Report : CS-TR-95-10 Abstract The fully connected recurrent network (FRN) using the on-line training method, Real Time Recurrent Learning (RTRL), is computationally expensive. It has a computational complexity of O(N 4 ) and storage complexity of O(N 3 ), where N is the number of non-input units. We have devised a locally connected recurrent model which has a much lower complexity in both computational time and storage space. The ring-structure recurrent network (RRN), the simplest kind of the locally connected has the corresponding complexity of O(mn+np) and O(np) respectively, where p, n and m are the number of input, hidden and output units respectively. We compare the performance between RRN and FRN in sequence recognition and time series prediction. We tested the networks' ability in temporal memorizing power and time warpping ability in the sequence recognition task. In the time series prediction task, we used both networks to train and predict three series; a periodic series with white noise, a deterministic chaotic series and the sunspots data. Both tasks show that RRN needs a much shorter training time and the performance of RRN is comparable to that of FRN. | [
283,
1990
] | Train |
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