Split (1)

train · 203k rows

node_id int64 0 76.9k	label int64 0 39	text stringlengths 13 124k	neighbors listlengths 0 3.32k	mask stringclasses 4 values
594	0	Title: Design and Implementation of a Replay Framework based on a Partial Order Planner Abstract: In this paper we describe the design and implementation of the derivation replay framework, dersnlp+ebl (Derivational snlp+ebl), which is based within a partial order planner. dersnlp+ebl replays previous plan derivation...	[ 593, 1122, 1194, 1621 ]	Test
595	2	Title: LEARNING TO CONTROL FAST-WEIGHT MEMORIES: AN ALTERNATIVE TO DYNAMIC RECURRENT NETWORKS (Neural Computation, 4(1):131-139, 1992) Abstract: Previous algorithms for supervised sequence learning are based on dynamic recurrent networks. This paper describes an alternative class of gradient-based systems consisting ...	[ 121, 233 ]	Test
596	6	Title: The Disk-Covering Method for Tree Reconstruction Abstract: Evolutionary tree reconstruction is a very important step in many biological research problems, and yet is extremely difficult for a variety of computational, statistical, and scientific reasons. In particular, the reconstruction of very large trees co...	[ 299 ]	Train
597	5	Title: Learning Semantic Grammars with Constructive Inductive Logic Programming Abstract: Automating the construction of semantic grammars is a difficult and interesting problem for machine learning. This paper shows how the semantic-grammar acquisition problem can be viewed as the learning of search-control heuristi...	[ 106, 204, 224, 434, 675 ]	Train
598	5	Title: Dynamic Hammock Predication for Non-predicated Instruction Set Architectures Abstract: Conventional speculative architectures use branch prediction to evaluate the most likely execution path during program execution. However, certain branches are difficult to predict. One solution to this problem is to evaluat...	[ 158, 302, 307, 428, 432 ]	Train
599	2	Title: A Model of Visually Guided Plasticity of the Auditory Spatial Map in the Barn Owl Abstract: In the barn owl, the self-organization of the auditory map of space in the external nucleus of the inferior colliculus (ICx) is strongly influenced by vision, but the nature of this interaction is unknown. In this paper...	[ 747 ]	Validation
600	2	Title: Separating hippocampal maps Spatial Functions of the Hippocampal Formation and the Abstract: The place fields of hippocampal cells in old animals sometimes change when an animal is removed from and then returned to an environment [ Barnes et al., 1997 ] . The ensemble correlation between two sequential visits...	[ 205, 745, 747, 1052 ]	Train
601	4	Title: Active Gesture Recognition using Partially Observable Markov Decision Processes Abstract: M.I.T Media Laboratory Perceptual Computing Section Technical Report No. 367 Appeared 13th IEEE Intl. Conference on Pattern Recognition (ICPR '96), Vienna, Austria. Abstract We present a foveated gesture recognition syste...	[ 3, 552, 565, 611, 1741 ]	Validation
602	1	Title: Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared Abstract: Various extensions to the Genetic Algorithm (GA) attempt to find all or most optima in a search space containing several optima. Many of these emulate natural speciation. For co-evolutionary learning to succeed in a ran...	[ 163, 1114, 2334 ]	Train
603	0	Title: METHOD-SPECIFIC KNOWLEDGE COMPILATION: TOWARDS PRACTICAL DESIGN SUPPORT SYSTEMS Abstract: Modern knowledge systems for design typically employ multiple problem-solving methods which in turn use different kinds of knowledge. The construction of a heterogeneous knowledge system that can support practical design ...	[ 540, 670, 1047, 1640 ]	Test
604	2	Title: First experiments using a mixture of nonlinear experts for time series prediction Abstract: This paper investigates the advantages and disadvantages of the mixture of experts (ME) model (introduced to the connectionist community in [JJNH91] and applied to time series analysis in [WM95]) on two time series wher...	[ 74, 668 ]	Test
605	2	Title: Learning Viewpoint Invariant Representations of Faces in an Attractor Network Abstract: In natural visual experience, different views of an object tend to appear in close temporal proximity as an animal manipulates the object or navigates around it. We investigated the ability of an attractor network to acquir...	[ 476, 576, 676 ]	Test
606	1	Title: Analysis of the Numerical Effects of Parallelism on a Parallel Genetic Algorithm Abstract: This paper examines the effects of relaxed synchronization on both the numerical and parallel efficiency of parallel genetic algorithms (GAs). We describe a coarse-grain geographically structured parallel genetic algorit...	[ 163, 537 ]	Test
607	2	Title: A Support Vector Machine Approach to Decision Trees Abstract: Key ideas from statistical learning theory and support vector machines are generalized to decision trees. A support vector machine is used for each decision in the tree. The "optimal" decision tree is characterized, and both a primal and dual space ...	[ 438, 821, 1055, 1306 ]	Test
608	2	Title: Regularization Theory and Neural Networks Architectures Abstract: We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smoothness functionals lead to a subcla...	[ 133, 179, 394, 611, 680, 975, 2230 ]	Validation
609	2	Title: Interactive Segmentation of Three-dimensional Medical Images (Extended abstract) Abstract: We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smoothness fun...	[ 719, 747 ]	Train
610	2	Title: Figure 1: The architecture of a Kohonen network. Each input neuron is fully connected with Abstract: We had previously shown that regularization principles lead to approximation schemes which are equivalent to networks with one layer of hidden units, called Regularization Networks. In particular, standard smooth...	[ 427, 747 ]	Train
611	2	Title: Learning networks for face analysis and synthesis Abstract: This paper presents an overview of the face-related projects in our group. The unifying theme underlying our work is the use of example-based learning methods for both analyzing and synthesizing face images. We label the example face images (and for t...	[ 28, 133, 179, 368, 386, 413, 511, 579, 601, 608, 633, 716, 718, 719, 864, 899, 938, 1079, 1103, 1116, 1265, 1315, 1352, 1488, 1493, 1499, 1564, 1668, 1732, 1755, 1763, 1935, 2050, 2230, 2260, 2325, 2340, 2352, 2378, 2385, 2501, 2505, ...	Validation
612	0	Title: Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases Abstract: This article describes how a reasoner can improve its understanding of an incompletely understood domain through the application of what it already knows to novel problems in that domain. Case-based reasoning is the proce...	[ 289, 629, 1046, 1348, 2568 ]	Validation
613	2	Title: A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA Abstract: We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence (Stormo & Haussler 1994). Probabilities are assigne...	[ 14, 232, 268, 616, 2107, 2496, 2571 ]	Validation
614	6	Title: Optimality and Domination in Repeated Games with Bounded Players Abstract: We examine questions of optimality and domination in repeated stage games where one or both players may draw their strategies only from (perhaps different) computationally bounded sets. We also consider optimality and domination when bo...	[ 615 ]	Validation
615	6	Title: Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries Abstract: We study the problem of efficiently learning to play a game optimally against an unknown adversary chosen from a computationally bounded class. We both contribute to the line of research on playing ga...	[ 54, 98, 555, 556, 614 ]	Train
616	2	Title: A Decision Tree System for Finding Genes in DNA Abstract: MORGAN is an integrated system for finding genes in vertebrate DNA sequences. MORGAN uses a variety of techniques to accomplish this task, the most distinctive of which is a decision tree classifier. The decision tree system is combined with new methods...	[ 268, 438, 613, 2046 ]	Train
617	2	Title: The Use of Neural Networks to Support "Intelligent" Scientific Computing Abstract: In this paper we report on the use of backpropagation based neural networks to implement a phase of the computational intelligence process of the PYTHIA[3] expert system for supporting the numerical simulation of applications mo...	[ 226 ]	Validation
618	6	Title: 0 Inductive learning of compact rule sets by using efficient hypotheses reduction Abstract: A method is described which reduces the hypotheses space with an efficient and easily interpretable reduction criteria called a - reduction. A learning algorithm is described based on a - reduction and analyzed by using...	[ 478, 638 ]	Train
619	3	Title: MEDIATING INSTRUMENTAL VARIABLES Abstract: A method is described which reduces the hypotheses space with an efficient and easily interpretable reduction criteria called a - reduction. A learning algorithm is described based on a - reduction and analyzed by using probability approximate correct learning results...	[ 260 ]	Train
620	2	Title: How Receptive Field Parameters Affect Neural Learning Abstract: We identify the three principle factors affecting the performance of learning by networks with localized units: unit noise, sample density, and the structure of the target function. We then analyze the effect of unit receptive field parameters on ...	[ 747 ]	Train
621	4	Title: Reinforcement Learning Methods for Continuous-Time Markov Decision Problems Abstract: Semi-Markov Decision Problems are continuous time generalizations of discrete time Markov Decision Problems. A number of reinforcement learning algorithms have been developed recently for the solution of Markov Decision Probl...	[ 471, 552, 565, 738, 1859 ]	Train
622	2	Title: CLASSIFICATION USING HIERARCHICAL MIXTURES OF EXPERTS Abstract: There has recently been widespread interest in the use of multiple models for classification and regression in the statistics and neural networks communities. The Hierarchical Mixture of Experts (HME) [1] has been successful in a number of regress...	[ 74, 345, 377 ]	Validation
623	3	Title: State-Space Abstraction for Anytime Evaluation of Probabilistic Networks Abstract: One important factor determining the computa - tional complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes. By varying the granularity of the state spaces, one can trade off accura...	[ 637, 1064, 1172, 1937, 2140, 2341 ]	Test
624	6	Title: Measuring the Difficulty of Specific Learning Problems Abstract: Existing complexity measures from contemporary learning theory cannot be conveniently applied to specific learning problems (e.g., training sets). Moreover, they are typically non-generic, i.e., they necessitate making assumptions about the way i...	[ 11, 112, 163, 572, 659, 700 ]	Test
625	2	Title: Statistical Biases in Backpropagation Learning Keywords: Cognitive Science, Pattern recognition Abstract: The paper investigates the statistical effects which may need to be exploited in supervised learning. It notes that these effects can be classified according to their conditionality and their order and pr...	[ 659, 700 ]	Validation
626	2	Title: Scatter-partitioning RBF network for function regression and image Abstract: segmentation: Preliminary results Abstract. Scatter-partitioning Radial Basis Function (RBF) networks increase their number of degrees of freedom with the complexity of an input-output mapping to be estimated on the basis of a supervi...	[ 261, 687 ]	Validation
627	2	Title: Learning Symbolic Rules Using Artificial Neural Networks Abstract: A distinct advantage of symbolic learning algorithms over artificial neural networks is that typically the concept representations they form are more easily understood by humans. One approach to understanding the representations formed by neura...	[ 338, 1057, 1270, 1562, 1679 ]	Test
628	2	Title: Figure 8: time complexity of unit parallelism measured on MANNA theoretical prediction #nodes N time Abstract: Our experience showed us that exibility in expressing a parallel algorithm for simulating neural networks is desirable even if it is not possible then to obtain the most efficient solution for any singl...	[ 747 ]	Train
629	0	Title: Using Introspective Reasoning to Select Learning Strategies Abstract: In order to learn effectively, a system must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it performs a given task and what particular pieces of knowledge...	[ 221, 612, 1535, 1537 ]	Train
630	2	Title: Input to State Stabilizability for Parameterized Families of Systems Key Words: Nonlinear stability, Robust control, Abstract: In order to learn effectively, a system must not only possess knowledge about the world and be able to improve that knowledge, but it also must introspectively reason about how it perfor...	[ 408, 447 ]	Validation
631	2	Title: Extracting Provably Correct Rules from Artificial Neural Networks Abstract: Although connectionist learning procedures have been applied successfully to a variety of real-world scenarios, artificial neural networks have often been criticized for exhibiting a low degree of comprehensibility. Mechanisms that aut...	[ 383, 1057, 1562 ]	Validation
632	3	Title: Toward Optimal Feature Selection Abstract: In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computation-ally intractable, method for feature subset selection is presented. We show that our goal should...	[ 401, 430, 635, 683, 1582, 1909, 2508 ]	Train
633	4	Title: Chapter 1 Reinforcement Learning for Planning and Control Abstract: In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computation-ally intractable, method for feature subset selection is presented. We ...	[ 197, 294, 565, 566, 611, 1459 ]	Train
634	0	Title: Oblivious Decision Trees and Abstract Cases Abstract: In this paper, we address the problem of case-based learning in the presence of irrelevant features. We review previous work on attribute selection and present a new algorithm, Oblivion, that carries out greedy pruning of oblivious decision trees, which eff...	[ 256, 430, 635, 686, 1513, 1570, 2593 ]	Train
635	6	Title: Learning Boolean Concepts in the Presence of Many Irrelevant Features Abstract: In this paper, we address the problem of case-based learning in the presence of irrelevant features. We review previous work on attribute selection and present a new algorithm, Oblivion, that carries out greedy pruning of oblivious...	[ 89, 109, 172, 177, 208, 256, 264, 375, 381, 430, 436, 442, 474, 632, 634, 640, 651, 660, 683, 686, 722 ]	Validation
636	4	Title: Robot Shaping: Developing Situated Agents through Learning Abstract: Learning plays a vital role in the development of situated agents. In this paper, we explore the use of reinforcement learning to "shape" a robot to perform a predefined target behavior. We connect both simulated and real robots to A LECSYS, ...	[ 118, 294, 552, 764, 1573, 2027, 2173, 2174, 2204, 2233, 2672, 2687 ]	Train
637	3	Title: Computational complexity reduction for BN2O networks using similarity of states Abstract: Although probabilistic inference in a general Bayesian belief network is an NP-hard problem, inference computation time can be reduced in most practical cases by exploiting domain knowledge and by making appropriate appro...	[ 515, 623 ]	Test
638	6	Title: Learning a set of primitive actions with an Induction of decision trees. Machine Learning, 1(1):81-106, Abstract: Although probabilistic inference in a general Bayesian belief network is an NP-hard problem, inference computation time can be reduced in most practical cases by exploiting domain knowledge and by ma...	[ 89, 227, 264, 294, 296, 348, 375, 383, 441, 449, 521, 618, 701, 722, 779, 862, 1027, 1189, 1312, 1327, 1460, 1468, 1539, 1808, 1895, 1919, 2045, 2146, 2171, 2409, 2426, 2465, 2541, 2673 ]	Train
639	3	Title: Bayesian Unsupervised Learning of Higher Order Structure Abstract: Multilayer architectures such as those used in Bayesian belief networks and Helmholtz machines provide a powerful framework for representing and learning higher order statistical relations among inputs. Because exact probability calculations wi...	[ 250, 584, 1763 ]	Test
640	6	Title: Learning in the Presence of Malicious Errors Abstract: In this paper we study an extension of the distribution-free model of learning introduced by Valiant [23] (also known as the probably approximately correct or PAC model) that allows the presence of malicious errors in the examples given to a learning algor...	[ 20, 109, 130, 199, 287, 459, 549, 574, 591, 635, 732, 1363, 1897, 2054, 2475 ]	Test
641	3	Title: Comparing Bayesian Model Class Selection Criteria by Discrete Finite Mixtures Abstract: We investigate the problem of computing the posterior probability of a model class, given a data sample and a prior distribution for possible parameter settings. By a model class we mean a group of models which all share th...	[ 376, 484, 1739 ]	Train
642	3	Title: Constructing Bayesian finite mixture models by the EM algorithm Abstract: Email: Firstname.Lastname@cs.Helsinki.FI Report C-1996-9, University of Helsinki, Department of Computer Science. Abstract In this paper we explore the use of finite mixture models for building decision support systems capable of sound p...	[ 484, 697 ]	Train
643	0	Title: Modeling Case-based Planning for Repairing Reasoning Failures Abstract: One application of models of reasoning behavior is to allow a reasoner to introspectively detect and repair failures of its own reasoning process. We address the issues of the transferability of such models versus the specificity of the kn...	[ 49, 50, 150, 222, 583, 1121, 1904, 2489 ]	Train
644	4	Title: Multi-criteria reinforcement learning Abstract: We consider multi-criteria sequential decision making problems, where the criteria are ordered according to their importance. Structural properties of these problems are touched and reinforcement learning algorithms, which learn asymptotically optimal decisions, ...	[ 210, 552, 565 ]	Train
645	3	Title: On the Markov Equivalence of Chain Graphs, Undirected Graphs, and Acyclic Digraphs Abstract: Graphical Markov models use undirected graphs (UDGs), acyclic directed graphs (ADGs), or (mixed) chain graphs to represent possible dependencies among random variables in a multivariate distribution. Whereas a UDG is u...	[ 51, 211, 312, 742 ]	Validation
646	3	Title: Constructing Computationally Efficient Bayesian Models via Unsupervised Clustering Probabilistic Reasoning and Bayesian Belief Networks, Abstract: Given a set of samples of an unknown probability distribution, we study the problem of constructing a good approximative Bayesian network model of the probability ...	[ 450 ]	Train
647	3	Title: Balls and Urns Abstract: We use a simple and illustrative example to expose some of the main ideas of Evidential Probability. Specifically, we show how the use of an acceptance rule naturally leads to the use of intervals to represent probabilities, how change of opinion due to experience can be facilitated, a...	[ 81 ]	Test
648	2	Title: Reprint of: Sontag, E.D., "Remarks on stabilization and input-to-state stability," Abstract: We use a simple and illustrative example to expose some of the main ideas of Evidential Probability. Specifically, we show how the use of an acceptance rule naturally leads to the use of intervals to represent probabil...	[ 693 ]	Train
649	0	Title: Concept Learning and Heuristic Classification in Weak-Theory Domains 1 Abstract: We use a simple and illustrative example to expose some of the main ideas of Evidential Probability. Specifically, we show how the use of an acceptance rule naturally leads to the use of intervals to represent probabilities, how c...	[ 96, 166, 457, 479, 752, 1643, 2123, 2294, 2403, 2560, 2581 ]	Train
650	4	Title: Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks Abstract: This paper presents U-Tree, a reinforcement learning algorithm that uses selective attention and short-term memory to simultaneously address the intertwined problems of large perceptual state spaces and hidden state. By com...	[ 472, 483, 656, 1006, 1557 ]	Test
651	2	Title: A Monotonic Measure for Optimal Feature Selection Abstract: Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, ...	[ 430, 635 ]	Train
652	5	Title: ARB: A Hardware Mechanism for Dynamic Reordering of Memory References* Abstract: Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optima...	[ 86, 249, 373 ]	Train
653	4	Title: Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email Abstract: This paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy. While it is widely agreed that dialogue strategies should be formulated in terms of communicative int...	[ 552, 2480 ]	Train
654	5	Title: Incremental Reduced Error Pruning Abstract: This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments sh...	[ 135, 1260 ]	Train
655	2	Title: Determining Mental State from EEG Signals Using Parallel Implementations of Neural Networks Abstract: EEG analysis has played a key role in the modeling of the brain's cortical dynamics, but relatively little effort has been devoted to developing EEG as a limited means of communication. If several mental state...	[ 198, 747 ]	Train
656	4	Title: Reinforcement Learning: A Survey Abstract: This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinf...	[ 148, 650, 657, 773 ]	Validation
657	4	Title: Adding Memory to XCS Abstract: We add internal memory to the XCS classifier system. We then test XCS with internal memory, named XCSM, in non-Markovian environments with two and four aliasing states. Experimental results show that XCSM can easily converge to optimal solutions in simple environments; moreover, ...	[ 656, 1581 ]	Train
658	1	Title: Hill Climbing with Learning (An Abstraction of Genetic Algorithm) Abstract: Simple modification of standard hill climbing optimization algorithm by taking into account learning features is discussed. Basic concept of this approach is the socalled probability vector, its single entries determine probabilities o...	[ 163, 427, 1577 ]	Train
659	2	Title: Trading Spaces: Computation, Representation and the Limits of Uninformed Learning Abstract: fl Research on this paper was partly supported by a Senior Research Leave fellowship granted by the Joint Council (SERC/MRC/ESRC) Cognitive Science Human Computer Interaction Initiative to one of the authors (Clark). Th...	[ 11, 163, 397, 624, 625, 695 ]	Test
660	6	Title: Efficient Algorithms for Identifying Relevant Features Abstract: This paper describes efficient methods for exact and approximate implementation of the MIN-FEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This bias is useful for learning domains where many irreleva...	[ 635 ]	Validation
661	3	Title: A Statistical Approach to Decision Tree Modeling Abstract: A statistical approach to decision tree modeling is described. In this approach, each decision in the tree is modeled parametrically as is the process by which an output is generated from an input and a sequence of decisions. The resulting model yields...	[ 71, 74, 76, 1191 ]	Train
662	2	Title: Free Energy Minimization Algorithm for Decoding and Cryptanalysis three binary vectors: s of length N Abstract: where A is a binary matrix. Our task is to infer s given z and A, and given assumptions about the statistical properties of s and n. This problem arises in the decoding of a noisy signal transmitted us...	[ 181, 518 ]	Validation
663	2	Title: Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models Abstract: An intelligent system has to be capable of adapting to a constantly changing environment. It therefore, ought to be capable of learning from its perceptual interactions with it...	[ 496, 501, 2393 ]	Train
664	2	Title: Diffusion of Context and Credit Information in Markovian Models Abstract: This paper studies the problem of ergodicity of transition probability matrices in Marko-vian models, such as hidden Markov models (HMMs), and how it makes very difficult the task of learning to represent long-term context for sequential...	[ 350, 978 ]	Test
665	2	Title: Requirements and use of neural networks for industrial appli- Abstract: Modern industry of today needs flexible, adaptive and fault-tolerant methods for information processing. Several applications have shown that neural networks fulfill these requirements. In this paper application areas, in which neural netw...	[ 294, 427, 747 ]	Train
666	2	Title: NeuroPipe a neural network based system for pipeline inspec- Abstract: Gas, oil and other pipelines need to be inspected for corrosion and other defects at regular intervals. For this application Pipetronix GmbH (PTX) Karlsruhe has developed a special ultrasonic based probe. Based on the recorded wall thicknes...	[ 745 ]	Train
667	2	Title: Recognizing Handwritten Digits Using Mixtures of Linear Models Abstract: We construct a mixture of locally linear generative models of a collection of pixel-based images of digits, and use them for recognition. Different models of a given digit are used to capture different styles of writing, and new images ar...	[ 257, 480, 867, 1356, 1928, 1974, 2072, 2227, 2270, 2570 ]	Train
668	2	Title: Nonlinear gated experts for time series: discovering regimes and avoiding overfitting Abstract: In: International Journal of Neural Systems 6 (1995) p. 373 - 399. URL of this paper: ftp://ftp.cs.colorado.edu/pub/Time-Series/MyPapers/experts.ps.Z, or http://www.cs.colorado.edu/~andreas/Time-Series/MyPapers/expe...	[ 263, 310, 604, 975, 1315, 1508, 2413, 2414, 2562, 2683 ]	Train
669	5	Title: Drug design by machine learning: Modelling drug activity Abstract: This paper describes an approach to modelling drug activity using machine learning tools. Some experiments in modelling the quantitative structure-activity relationship (QSAR) using a standard, Hansch, method and a machine learning system Golem...	[ 509 ]	Validation
670	0	Title: Rule Based Database Integration in HIPED Heterogeneous Intelligent Processing in Engineering Design Abstract: In this paper 1 we describe one aspect of our research in the project called HIPED, which addressed the problem of performing design of engineering devices by accessing heterogeneous databases. The fro...	[ 603 ]	Train
671	4	Title: Learning to Achieve Goals Abstract: Temporal difference methods solve the temporal credit assignment problem for reinforcement learning. An important subproblem of general reinforcement learning is learning to achieve dynamic goals. Although existing temporal difference methods, such as Q learning, can be appl...	[ 552, 562, 565, 566, 1459, 1599 ]	Test
672	6	Title: Cryptographic Limitations on Learning Boolean Formulae and Finite Automata Abstract: In this paper we prove the intractability of learning several classes of Boolean functions in the distribution-free model (also called the Probably Approximately Correct or PAC model) of learning from examples. These results a...	[ 109, 130, 456, 535, 549, 574, 591, 924, 1003, 1004, 1293, 1343, 1363, 1386, 1460, 2004, 2040, 2329, 2360, 2653, 2696 ]	Train
673	2	Title: Increasing Consensus Accuracy in DNA Fragment Assemblies by Incorporating Fluorescent Trace Representations Abstract: We present a new method for determining the consensus sequence in DNA fragment assemblies. The new method, Trace-Evidence, directly incorporates aligned ABI trace information into consensus cal...	[ 194 ]	Test
674	2	Title: Transfer in a Connectionist Model of the Acquisition of Morphology Abstract: The morphological systems of natural languages are replete with examples of the same devices used for multiple purposes: (1) the same type of morphological process (for example, suffixation for both noun case and verb tense) and (2) i...	[ 427 ]	Train
675	5	Title: Combining FOIL and EBG to Speed-up Logic Programs Abstract: This paper presents an algorithm that combines traditional EBL techniques and recent developments in inductive logic programming to learn effective clause selection rules for Prolog programs. When these control rules are incorporated into the original...	[ 344, 597, 1429, 1442, 1445, 2215, 2339 ]	Validation
676	2	Title: A Model of Invariant Object Recognition in the Visual System Abstract: Neurons in the ventral stream of the primate visual system exhibit responses to the images of objects which are invariant with respect to natural transformations such as translation, size, and view. Anatomical and neurophysiological evidenc...	[ 605, 1014, 1056, 1091 ]	Train
677	2	Title: Recurrent Neural Networks for Missing or Asynchronous Data Abstract: In this paper we propose recurrent neural networks with feedback into the input units for handling two types of data analysis problems. On the one hand, this scheme can be used for static data when some of the input variables are missing. On ...	[ 71 ]	Train
678	2	Title: Algebraic Transformations of Objective Functions Abstract: Many neural networks can be derived as optimization dynamics for suitable objective functions. We show that such networks can be designed by repeated transformations of one objective into another with the same fixpoints. We exhibit a collection of alge...	[ 528 ]	Validation
679	0	Title: Developing Case-Based Reasoning for Structural Design Abstract: The use of case-based reasoning as a process model of design involves the subtasks of recalling previously known designs from memory and adapting these design cases or subcases to fit the current design context. The development of this process mod...	[ 30, 32 ]	Train
680	2	Title: Cortical Mechanisms of Visual Recognition and Learning: A Hierarchical Kalman Filter Model Abstract: We describe a biologically plausible model of dynamic recognition and learning in the visual cortex based on the statistical theory of Kalman filtering from optimal control theory. The model utilizes a hierarch...	[ 74, 90, 608, 747 ]	Test
681	1	Title: Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution. Abstract: Individual lifetime learning can `guide' an evolving population to areas of high fitness in genotype space through an evolutionary phenomenon known as the Baldwin effect (Baldwin, 1896; Hin-ton & Nowlan, 1987). It ...	[ 402, 712, 2309 ]	Validation
682	4	Title: Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators Abstract: This paper focuses on the optimization of hyper-parameters for function approximators. We describe a kind of racing algorithm for continuous optimization problems that spends less time evaluating poor parameter s...	[ 88, 1791, 2430 ]	Train
683	0	Title: On the Greediness of Feature Selection Algorithms Abstract: Based on our analysis and experiments using real-world datasets, we find that the greediness of forward feature selection algorithms does not severely corrupt the accuracy of function approximation using the selected input features, but improves the e...	[ 430, 632, 635, 686, 1860 ]	Train
684	3	Title: Finding Overlapping Distributions with MML Abstract: This paper considers an aspect of mixture modelling. Significantly overlapping distributions require more data for their parameters to be accurately estimated than well separated distributions. For example, two Gaussian distributions are considered to signif...	[ 161, 525, 779, 1425, 1550 ]	Train
685	2	Title: Performance of the GCel-512 and PowerXPlorer for parallel neural network simulations Abstract: This report presents new results from work performed in the framework of the IC 3 A pro-gramme. Using the GCel-512 and the PowerXPlorer made available by the UvA, a performance prediction model for several neural net...	[ 217, 747 ]	Validation
686	0	Title: Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms Abstract: With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term prototypes refers to the refer...	[ 44, 116, 119, 223, 225, 318, 381, 430, 634, 635, 683, 760, 1273, 1698, 2541, 2557 ]	Train
687	2	Title: Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning Abstract: We present a new self-organizing neural network model having two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization. The main ...	[ 110, 626, 741, 745, 1157, 1564, 1700, 1704, 2381, 2683 ]	Train
688	4	Title: Exploration and Model Building in Mobile Robot Domains Abstract: I present first results on COLUMBUS, an autonomous mobile robot. COLUMBUS operates in initially unknown, structured environments. Its task is to explore and model the environment efficiently while avoiding collisions with obstacles. COLUMBUS uses...	[ 60, 207, 252, 412, 552, 566, 835, 1248 ]	Train
689	1	Title: Where Genetic Algorithms Excel Abstract: We analyze the performance of a Genetic Algorithm (GA) we call Culling and a variety of other algorithms on a problem we refer to as Additive Search Problem (ASP). ASP is closely related to several previously well studied problems, such as the game of Mastermind and add...	[ 163, 967, 1580, 1826 ]	Train
690	0	Title: Instance Pruning Techniques Abstract: The nearest neighbor algorithm and its derivatives are often quite successful at learning a concept from a training set and providing good generalization on subsequent input vectors. However, these techniques often retain the entire training set in memory, resulting in lar...	[ 445, 2457 ]	Train
691	4	Title: Reinforcement Learning in the Multi-Robot Domain Abstract: This paper describes a formulation of reinforcement learning that enables learning in noisy, dynamic environemnts such as in the complex concurrent multi-robot learning domain. The methodology involves minimizing the learning space through the use beha...	[ 552, 565, 1557, 1649, 2658 ]	Test
692	6	Title: Decision Tree Induction: How Effective is the Greedy Heuristic? Abstract: Most existing decision tree systems use a greedy approach to induce trees \| locally optimal splits are induced at every node of the tree. Although the greedy approach is suboptimal, it is believed to produce reasonably good trees. In the...	[ 296, 378, 438, 1191 ]	Train
693	2	Title: FURTHER FACTS ABOUT INPUT TO STATE STABILIZATION "Further facts about input to state stabilization", IEEE Abstract: Report SYCON-88-15 ABSTRACT Previous results about input to state stabilizability are shown to hold even for systems which are not linear in controls, provided that a more general type of feedback ...	[ 531, 648, 1471, 1501 ]	Train

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