node_id int64 0 76.9k | label int64 0 39 | text stringlengths 13 124k | neighbors listlengths 0 3.32k | mask stringclasses 4
values |
|---|---|---|---|---|
1,094 | 2 | Title: Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA
Abstract: Infusion of a GABA agonist (Reiter & Stryker, 1988) and infusion of an NMDA receptor antagonist (Bear et al., 1990), in the primary visual cortex of kittens during monocular deprivation, shifts ocular domina... | [
355,
1659,
2085,
2228
] | Validation |
1,095 | 6 | Title: Learning Unions of Boxes with Membership and Equivalence Queries
Abstract: We present two algorithms that use membership and equivalence queries to exactly identify the concepts given by the union of s discretized axis-parallel boxes in d-dimensional discretized Euclidean space where each coordinate can have n... | [
792,
798,
1360,
1433,
1456
] | Validation |
1,096 | 1 | Title: Pruning backpropagation neural networks using modern stochastic optimization techniques
Abstract: Approaches combining genetic algorithms and neural networks have received a great deal of attention in recent years. As a result, much work has been reported in two major areas of neural network design: training a... | [
1069
] | Train |
1,097 | 3 | Title: Belief Propagation and Revision in Networks with Loops
Abstract: Local belief propagation rules of the sort proposed by Pearl (1988) are guaranteed to converge to the optimal beliefs for singly connected networks. Recently, a number of researchers have empirically demonstrated good performance of these same al... | [
1393
] | Train |
1,098 | 1 | Title: Scheduling Maintenance of Electrical Power Transmission Networks Using Genetic Programming
Abstract: Previous work showed the combination of a Genetic Algorithm using an order or permutation chromosome combined with hand coded "Greedy" Optimizers can readily produce an optimal schedule for a four node test pro... | [
163,
343,
1305,
1911
] | Train |
1,099 | 1 | Title: A Methodology for Processing Problem Constraints in Genetic Programming
Abstract: Search mechanisms of artificial intelligence combine two elements: representation, which determines the search space, and a search mechanism, which actually explores the space. Unfortunately, many searches may explore redundant a... | [
163,
1178
] | Train |
1,100 | 2 | Title: Observability of Linear Systems with Saturated Outputs
Abstract: In this paper, we present necessary and sufficient conditions for observability of the class of output-saturated systems. These are linear systems whose output passes through a saturation function before it can be measured. | [
1464
] | Train |
1,101 | 0 | Title: On Reasoning from Data
Abstract: In this paper, we present necessary and sufficient conditions for observability of the class of output-saturated systems. These are linear systems whose output passes through a saturation function before it can be measured. | [
843,
1111,
1328
] | Train |
1,102 | 5 | Title: Automated Refinement of First-Order Horn-Clause Domain Theories
Abstract: Knowledge acquisition is a difficult, error-prone, and time-consuming task. The task of automatically improving an existing knowledge base using learning methods is addressed by the class of systems performing theory refinement. This pap... | [
136,
985,
1174,
1370,
1413,
1479
] | Validation |
1,103 | 2 | Reference: [39] <author> Yoda, M. </author> <year> (1994). </year> <title> Predicting the Tokyo stock market. </title> <editor> In Deboeck, G.J. (Ed.) </editor> <year> (1994). </year> <title> Trading on the Edge. </title> <address> New York: </address> <publisher> Wiley., </publisher> <pages> 66-79. </pages> <instituti... | [
74,
427,
611,
1079,
1579
] | Train |
1,104 | 6 | Title: Feature Generation for Sequence Categorization
Abstract: The problem of sequence categorization is to generalize from a corpus of labeled sequences procedures for accurately labeling future unlabeled sequences. The choice of representation of sequences can have a major impact on this task, and in the absence o... | [
1260,
1262
] | Test |
1,105 | 6 | Title: PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract)
Abstract: | [
591,
798,
1026,
2356
] | Validation |
1,106 | 1 | Title: Genetic Algorithms for Combinatorial Optimization: The Assembly Line Balancing Problem
Abstract: Genetic algorithms are one example of the use of a random element within an algorithm for combinatorial optimization. We consider the application of the genetic algorithm to a particular problem, the Assembly Line ... | [
163,
1065,
1305
] | Train |
1,107 | 0 | Title: The Possible Contribution of AI to the Avoidance of Crises and Wars: Using CBR Methods
Abstract: This paper presents the application of Case-Based Reasoning methods to the KOSIMO data base of international conflicts. A Case-Based Reasoning tool - VIE-CBR has been deveolped and used for the classification of vari... | [
1328,
1617
] | Test |
1,108 | 3 | Title: Confidence as Higher Order Uncertainty proposed for handling higher order uncertainty, including the Bayesian approach,
Abstract: | [
1503,
1504,
1506,
1507
] | Train |
1,109 | 6 | Title: Inductive Bias in Case-Based Reasoning Systems
Abstract: In order to learn more about the behaviour of case-based reasoners as learning systems, we form-alise a simple case-based learner as a PAC learning algorithm, using the case-based representation hCB; i. We first consider a `naive' case-based learning alg... | [
1328,
1570,
2151
] | Train |
1,110 | 1 | Title: On The State of Evolutionary Computation
Abstract: In the past few years the evolutionary computation landscape has been rapidly changing as a result of increased levels of interaction between various research groups and the injection of new ideas which challenge old tenets. The effect has been simultaneously ... | [
163,
793,
1016,
1728,
1729
] | Train |
1,111 | 0 | Title: Towards a Better Understanding of Memory-Based Reasoning Systems
Abstract: We quantify both experimentally and analytically the performance of memory-based reasoning (MBR) algorithms. To start gaining insight into the capabilities of MBR algorithms, we compare an MBR algorithm using a value difference metric t... | [
258,
1101,
1328,
1339,
1412,
1570,
2292
] | Test |
1,112 | 2 | Title: Flexible Metric Nearest Neighbor Classiflcation
Abstract: The K-nearest-neighbor decision rule assigns an object of unknown class to the plurality class among the K labeled "training" objects that are closest to it. Closeness is usually deflned in terms of a metric distance on the Euclidean space with the inpu... | [
101,
719,
926,
1073,
1403,
1512,
1618,
2415
] | Test |
1,113 | 1 | Title: Staged Hybrid Genetic Search for Seismic Data Imaging
Abstract: Seismic data interpretation problems are typically solved using computationally intensive local search methods which often result in inferior solutions. Here, a traditional hybrid genetic algorithm is compared with different staged hybrid genetic ... | [
163,
1153,
1380
] | Test |
1,114 | 1 | Title: Using Genetic Algorithms to Explore Pattern Recognition in the Immune System COMMENTS WELCOME
Abstract: This paper describes an immune system model based on binary strings. The purpose of the model is to study the pattern recognition processes and learning that take place at both the individual and species le... | [
163,
602,
1117,
1261,
1371,
1588,
1603,
1696
] | Train |
1,115 | 2 | Title: The locally linear Nested Network for robot manipulation
Abstract: We present a method for accurate representation of high-dimensional unknown functions from random samples drawn from its input space. The method builds representations of the function by recursively splitting the input space in smaller subspace... | [
820,
1252
] | Validation |
1,116 | 2 | Title: Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;
Abstract: Several authors have made a link between hidden Markov models for time series and energy-based models (Luttrell 1989, Williams 1990, Saul and Jordan 1995). Saul and Jordan (1995) discuss a linear Boltzmann chai... | [
31,
611,
708,
736,
1593
] | Train |
1,117 | 1 | Title: A Coevolutionary Approach to Learning Sequential Decision Rules
Abstract: We present a coevolutionary approach to learning sequential decision rules which appears to have a number of advantages over non-coevolutionary approaches. The coevolutionary approach encourages the formation of stable niches representin... | [
247,
562,
910,
1114,
1225,
1261,
1588,
1603,
2089,
2332
] | Train |
1,118 | 2 | Title: Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta
Abstract: Appropriate bias is widely viewed as the key to efficient learning and generalization. I present a new algorithm, the Incremental Delta-Bar-Delta (IDBD) algorithm, for the learning of appropriate biases based on previous lea... | [
134,
1540
] | Train |
1,119 | 2 | Title: Adaptive Parameter Pruning in Neural Networks
Abstract: Neural network pruning methods on the level of individual network parameters (e.g. connection weights) can improve generalization. An open problem in the pruning methods known today (OBD, OBS, autoprune, epsiprune) is the selection of the number of parame... | [
881,
1203,
2405
] | Train |
1,120 | 2 | Title: ICSIM: An Object-Oriented Connectionist Simulator gives an overview of the simulator. Its main concepts, the
Abstract: ICSIM is a connectionist net simulator being developed at ICSI and written in Sather. It is object-oriented to meet the requirements for flexibility and reuse of homogeneous and structured conne... | [
1677,
2275
] | Train |
1,121 | 0 | Title: Generic Teleological Mechanisms and their Use in Case Adaptation
Abstract: In experience-based (or case-based) reasoning, new problems are solved by retrieving and adapting the solutions to similar problems encountered in the past. An important issue in experience-based reasoning is to identify different types... | [
540,
643,
806,
1046,
1138,
1344,
1640
] | Validation |
1,122 | 0 | Title: A Comparative Utility Analysis of Case-Based Reasoning and Control-Rule Learning Systems
Abstract: The utility problem in learning systems occurs when knowledge learned in an attempt to improve a system's performance degrades performance instead. We present a methodology for the analysis of utility problems wh... | [
578,
594,
717,
799,
1194,
1534
] | Train |
1,123 | 0 | Title: MAC/FAC: A Model of Similarity-based Retrieval
Abstract: We present a model of similarity-based retrieval which attempts to capture three psychological phenomena: (1) people are extremely good at judging similarity and analogy when given items to compare. (2) Superficial remindings are much more frequent than ... | [
75,
539,
541,
1176,
1188,
1354,
1483,
1674,
1680
] | Train |
1,124 | 6 | Title: ON-LINE LEARNING OF LINEAR FUNCTIONS
Abstract: We present an algorithm for the on-line learning of linear functions which is optimal to within a constant factor with respect to bounds on the sum of squared errors for a worst case sequence of trials. The bounds are logarithmic in the number of variables. Furthe... | [
453,
1566,
1567
] | Validation |
1,125 | 0 | Title: Constructive Similarity Assessment: Using Stored Cases to Define New Situations
Abstract: A fundamental issue in case-based reasoning is similarity assessment: determining similarities and differences between new and retrieved cases. Many methods have been developed for comparing input case descriptions to the... | [
166,
817,
818,
857,
1483,
1496
] | Validation |
1,126 | 0 | Title: Towards A Computer Model of Memory Search Strategy Learning
Abstract: Much recent research on modeling memory processes has focused on identifying useful indices and retrieval strategies to support particular memory tasks. Another important question concerning memory processes, however, is how retrieval criter... | [
580,
583,
923,
1212,
1497,
2371,
2372,
2489
] | Train |
1,127 | 1 | Title: Recombination Operator, its Correlation to the Fitness Landscape and Search Performance
Abstract: The author reserves all other publication and other rights in association with the copyright in the thesis, and except as hereinbefore provided, neither the thesis nor any substantial portion thereof may be printe... | [
163,
728,
793,
938,
1153
] | Train |
1,128 | 3 | Title: On Structured Variational Approximations
Abstract: The problem of approximating a probability distribution occurs frequently in many areas of applied mathematics, including statistics, communication theory, machine learning, and the theoretical analysis of complex systems such as neural networks. Saul and Jord... | [
76,
1288,
1393
] | Train |
1,129 | 2 | Title: A Self-Organizing Binary Decision Tree For Incrementally Defined Rule Based
Abstract: This paper presents an ASOCS (adaptive self-organizing concurrent system) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic... | [
26,
809,
814,
919,
1080,
1190,
1222
] | Train |
1,130 | 1 | Title: Dynamic Hill Climbing: Overcoming the limita- tions of optimization techniques
Abstract: This paper describes a novel search algorithm, called dynamic hill climbing, that borrows ideas from genetic algorithms and hill climbing techniques. Unlike both genetic and hill climbing algorithms, dynamic hill climbing ... | [
163,
959,
1334
] | Test |
1,131 | 1 | Title: ADAPTIVE TESTING OF CONTROLLERS FOR AUTONOMOUS VEHICLES
Abstract: Autonomous vehicles are likely to require sophisticated software controllers to maintain vehicle performance in the presence of vehicle faults. The test and evaluation of complex software controllers is expected to be a challenging task. The goa... | [
910,
966,
1253
] | Test |
1,132 | 6 | Title: A Theory of Unsupervised Speedup Learning
Abstract: Speedup learning seeks to improve the efficiency of search-based problem solvers. In this paper, we propose a new theoretical model of speedup learning which captures systems that improve problem solving performance by solving a user-given set of problems. We... | [
1309
] | Train |
1,133 | 3 | Title: A Fast Non-Parametric Density Estimation Algorithm
Abstract: Non-parametric density estimation is the problem of approximating the values of a probability density function, given samples from the associated distribution. Non-parametric estimation finds applications in discriminant analysis, cluster analysis, a... | [
719,
1666
] | Test |
1,134 | 1 | Title: Discontinuity in evolution: how different levels of organization imply pre-adaptation
Abstract: Non-parametric density estimation is the problem of approximating the values of a probability density function, given samples from the associated distribution. Non-parametric estimation finds applications in discrim... | [
1264,
2281
] | Test |
1,135 | 6 | Title: Learning First-Order Acyclic Horn Programs from Entailment
Abstract: In this paper, we consider learning first-order Horn programs from entailment. In particular, we show that any subclass of first-order acyclic Horn programs with constant arity is exactly learnable from equivalence and entailment membership q... | [
1174,
1442,
1444
] | Train |
1,136 | 1 | Title: Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems
Abstract: Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Since SAT is NP-Complete, any other NP-Complete problem can be transfo... | [
163,
727,
800,
935,
1018,
1030,
1060,
1063,
1142,
1286,
1333,
1516,
1523,
1558,
1575,
1594,
1740
] | Train |
1,137 | 4 | Title: Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates
Abstract: Fully cooperative multiagent systemsthose in which agents share a joint utility modelis of special interest in AI. A key problem is that of ensuring that the actions of individual agents are coordinated, especially in se... | [
558,
1459,
1687
] | Train |
1,138 | 0 | Title: Learning Generic Mechanisms from Experiences for Analogical Reasoning
Abstract: Humans appear to often solve problems in a new domain by transferring their expertise from a more familiar domain. However, making such cross-domain analogies is hard and often requires abstractions common to the source and target ... | [
806,
1121,
1344,
1420,
1597,
2706
] | Train |
1,139 | 1 | Title: Optimal Mutation Rates in Genetic Search
Abstract: The optimization of a single bit string by means of iterated mutation and selection of the best (a (1+1)-Genetic Algorithm) is discussed with respect to three simple fitness functions: The counting ones problem, a standard binary encoded integer, and a Gray co... | [
163,
780,
793,
1018,
1572,
1598
] | Train |
1,140 | 1 | Title: LEARNING ROBOT BEHAVIORS USING GENETIC ALGORITHMS
Abstract: Genetic Algorithms are used to learn navigation and collision avoidance behaviors for robots. The learning is performed under simulation, and the resulting behaviors are then used to control the The approach to learning behaviors for robots described ... | [
811,
910,
964,
965,
966,
981,
1311,
2294
] | Train |
1,141 | 3 | Title: Bayesian Graphical Modeling for Intelligent Tutoring Systems
Abstract: Conventional Intelligent Tutoring Systems (ITS) do not acknowledge uncertainty about the student's knowledge. Yet, both the outcome of any teaching intervention and the exact state of the student's knowledge are uncertain. In recent years, ... | [
1172,
1240,
1241
] | Test |
1,142 | 1 | Title: A NN Algorithm for Boolean Satisfiability Problems
Abstract: Satisfiability (SAT) refers to the task of finding a truth assignment that makes an arbitrary boolean expression true. This paper compares a neural network algorithm (NNSAT) with GSAT [4], a greedy algorithm for solving satisfiability problems. GSAT ... | [
1018,
1136
] | Train |
1,143 | 1 | Title: Neural Networks in an Artificial Life Perspective
Abstract: In the last few years several researchers within the Artificial Life and Mobile Robotics community used Artificial Neural Networks. Explicitly viewing Neural Networks in an Artificial Life perspective has a number of consequences that make research on... | [
1404,
2165,
2429
] | Train |
1,144 | 2 | Title: VIEWNET ARCHITECTURES FOR INVARIANT 3-D OBJECT LEARNING AND RECOGNITION FROM MULTIPLE 2-D VIEWS
Abstract: 3 The recognition of 3-D objects from sequences of their 2-D views is modeled by a family of self-organizing neural architectures, called VIEWNET, that use View Information Encoded With NETworks. VIEWNET i... | [
592,
1509
] | Test |
1,145 | 2 | Title: A Unifying View of Some Training Algorithms for Multilayer Perceptrons with FIR Filter Synapses
Abstract: Recent interest has come about in deriving various neural network architectures for modelling time-dependent signals. A number of algorithms have been published for multilayer perceptrons with synapses des... | [
1323
] | Train |
1,146 | 2 | Title: The optimal number of learning samples and hidden units in function approximation with a feedforward network
Abstract: This paper presents a methodology to estimate the optimal number of learning samples and the number of hidden units needed to obtain a desired accuracy of a function approximation by a feedfor... | [
820,
1676
] | Train |
1,147 | 3 | Title: Decomposable graphical Gaussian model determination
Abstract: We propose a methodology for Bayesian model determination in decomposable graphical Gaussian models. To achieve this aim we consider a hyper inverse Wishart prior distribution on the concentration matrix for each given graph. To ensure compatibility... | [
161,
772,
1240,
1241,
1347
] | Train |
1,148 | 0 | Title: Opportunistic Reasoning: A Design Perspective
Abstract: An essential component of opportunistic behavior is opportunity recognition, the recognition of those conditions that facilitate the pursuit of some suspended goal. Opportunity recognition is a special case of situation assessment, the process of sizing u... | [
30,
285,
486,
1355,
1534,
1597
] | Train |
1,149 | 2 | Title: What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation
Abstract: Technical Report UMIACS-TR-96-22 and CS-TR-3617 Institute for Advanced Computer Studies University of Maryland College Park, MD 20742 Abstract One of the most important aspects of any machine learning par... | [
912,
1150,
1323,
1891,
2044
] | Test |
1,150 | 2 | Title: Lessons in Neural Network Training: Overfitting Lessons in Neural Network Training: Overfitting May be Harder
Abstract: For many reasons, neural networks have become very popular AI machine learning models. Two of the most important aspects of machine learning models are how well the model generalizes to unseen ... | [
912,
1149,
1323,
1630
] | Train |
1,151 | 5 | Title: Learning Classification Rules Using Lattices
Abstract: This paper presents a novel induction algorithm, Rulearner, which induces classification rules using a Galois lattice as an explicit map through the search space of rules. The construction of lattices from data is initially discussed and the use of these s... | [
1350
] | Train |
1,152 | 0 | Title: Fish and Shrink. A next step towards e-cient case retrieval in large scaled case bases
Abstract: Keywords: Case-Based Reasoning, case retrieval, case representation This paper deals with the retrieval of useful cases in case-based reasoning. It focuses on the questions of what "useful" could mean and how the s... | [
1453
] | Test |
1,153 | 1 | Title: Evolution in Time and Space The Parallel Genetic Algorithm
Abstract: The parallel genetic algorithm (PGA) uses two major modifications compared to the genetic algorithm. Firstly, selection for mating is distributed. Individuals live in a 2-D world. Selection of a mate is done by each individual independently i... | [
163,
856,
942,
1063,
1065,
1070,
1077,
1113,
1127,
1204,
1205,
1219,
1257,
1410,
1455,
1611,
1675
] | Test |
1,154 | 0 | Title: Case-Based Learning: Beyond Classification of Feature Vectors
Abstract: The dominant theme of case-based research at recent ML conferences has been on classifying cases represented by feature vectors. However, other useful tasks can be targeted, and other representations are often preferable. We review the rec... | [
819,
1531
] | Train |
1,155 | 0 | Title: Memory-Based Lexical Acquisition and Processing
Abstract: Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability ... | [
783,
785,
862,
1328,
1407,
1601,
1812
] | Test |
1,156 | 3 | Title: A NEW SEQUENTIAL SIMULATED ANNEALING METHOD
Abstract: Let H be a function not explicitly defined, but approximable by a sequence (H n ) n0 of functional estimators. In this context we propose a new sequential algorithm to optimise asymptotically H using stepwise estimators H n . We prove under mild conditions ... | [
1013
] | Validation |
1,157 | 2 | Title: Some Competitive Learning Methods (Some additions and refinements are planned for
Abstract: Let H be a function not explicitly defined, but approximable by a sequence (H n ) n0 of functional estimators. In this context we propose a new sequential algorithm to optimise asymptotically H using stepwise estimator... | [
687,
741,
745,
1700,
1704
] | Train |
1,158 | 3 | Title: Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data
Abstract: In this paper we study a new information-theoretically justified approach to missing data estimation for multivariate categorical data. The approach discussed is a model-based imputation procedure relative to a model cla... | [
1550,
1555
] | Validation |
1,159 | 1 | Title: An evolutionary tabu search algorithm and the NHL scheduling problem
Abstract: We present in this paper a new evolutionary procedure for solving general optimization problems that combines efficiently the mechanisms of genetic algorithms and tabu search. In order to explore the solution space properly interact... | [
163,
1485,
2564
] | Test |
1,160 | 6 | Title: PFSA Modelling of Behavioural Sequences by Evolutionary Programming Rockhampton, Queensland. (1994) "PFSA Modelling of Behavioural
Abstract: Behavioural observations can often be described as a sequence of symbols drawn from a finite alphabet. However the inductive inference of such strings by any automated tech... | [
1166
] | Train |
1,161 | 6 | Title: Inductive Learning by Selection of Minimal Complexity Representations
Abstract: Behavioural observations can often be described as a sequence of symbols drawn from a finite alphabet. However the inductive inference of such strings by any automated technique to produce models of the data is a nontrivial task. T... | [
1560,
1592,
1702,
2324,
2423,
2657
] | Train |
1,162 | 3 | Title: Signal Processing and Communications Reversible Jump Sampler for Autoregressive Time Series, Employing Full Conditionals to
Abstract: Technical Report CUED/F-INFENG/TR. 304 We use reversible jump Markov chain Monte Carlo (MCMC) methods (Green 1995) to address the problem of model order uncertainty in au-toregres... | [
1613
] | Test |
1,163 | 0 | Title: Case-Based Planning to Learn
Abstract: Learning can be viewed as a problem of planning a series of modifications to memory. We adopt this view of learning and propose the applicability of the case-based planning methodology to the task of planning to learn. We argue that relatively simple, fine-grained primiti... | [
1497,
1498,
1534
] | Test |
1,164 | 6 | Title: PAC Analyses of a `Similarity Learning' IBL Algorithm
Abstract: V S-CBR [14] is a simple instance-based learning algorithm that adjusts a weighted similarity measure as well as collecting cases. This paper presents a `PAC' analysis of V S-CBR, motivated by the PAC learning framework, which demonstrates two mai... | [
1570,
1584,
1626
] | Train |
1,165 | 5 | Title: Discovering Compressive Partial Determinations in Mixed Numerical and Symbolic Domains
Abstract: Partial determinations are an interesting form of dependency between attributes in a relation. They generalize functional dependencies by allowing exceptions. We modify a known MDL formula for evaluating such parti... | [
430,
1327
] | Train |
1,166 | 6 | Title: Assessment of candidate pfsa models induced from symbol datasets
Abstract: The induction of the optimal finite state machine explanation from symbol strings is known to be at least NP-complete. However, satisfactory approximately optimal explanations may be found by the use of Evolutionary Programming. It has ... | [
1160
] | Train |
1,167 | 1 | Title: Evolving Globally Synchronized Cellular Automata
Abstract: How does an evolutionary process interact with a decentralized, distributed system in order to produce globally coordinated behavior? Using a genetic algorithm (GA) to evolve cellular automata (CAs), we show that the evolution of spontaneous synchroniz... | [
1330,
1331,
1332
] | Test |
1,168 | 3 | Title: Bootstrapping Z Estimators
Abstract: We prove a general bootstrap theorem for possibly infinite-dimensional Zestimators which builds on the recent infinite-dimensional Ztheorem due to Van der Vaart (1995). Our result extends finite-dimensional results of this type for the bootstrap due to Arcones and Gine (199... | [
802
] | Train |
1,169 | 2 | Title: Individual and Collective Prognostic Prediction
Abstract: The prediction of survival time or recurrence time is an important learning problem in medical domains. The Recurrence Surface Approximation (RSA) method is a natural, effective method for predicting recurrence times using censored input data. This pape... | [
524,
1284,
1454
] | Validation |
1,170 | 6 | Title: Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee
Abstract: We analyze the "query by committee" algorithm, a method for filtering informative queries from a random stream of inputs. We show that if the two-member committee algorithm achieves in... | [
517,
1198
] | Train |
1,171 | 2 | Title: Nonlinear Component Analysis as a Kernel Eigenvalue Problem
Abstract: A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input... | [
1050
] | Train |
1,172 | 3 | Title: Introduction to the Special Section on Knowledge-Based Construction of Probabilistic and Decision Models (IEEE Transactions
Abstract: Modeling techniques developed recently in the AI and uncertain reasoning communities permit significantly more flexible specifications of probabilistic knowledge. Specifically, gr... | [
623,
915,
1141,
2108,
2341
] | Test |
1,173 | 6 | Title: Dynamical Selection of Learning Algorithms
Abstract: Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain have met with limited success. This paper proposes a new approach to predicti... | [
318,
1328,
2583
] | Train |
1,174 | 6 | Title: LEARNING CONCEPTS BY ASKING QUESTIONS
Abstract: Tw o important issues in machine learning are explored: the role that memory plays in acquiring new concepts; and the extent to which the learner can take an active part in acquiring these concepts. This chapter describes a program, called Marvin, which uses conc... | [
303,
414,
893,
902,
1033,
1074,
1102,
1135,
1297
] | Train |
1,175 | 1 | Title: Complex Environments to Complex Behaviors FROM COMPLEX ENVIRONMENTS TO COMPLEX BEHAVIORS
Abstract: Adaptation of ecological systems to their environments is commonly viewed through some explicit fitness function defined a priori by the experimenter, or measured a posteriori by estimations based on population ... | [
1325,
1628
] | Train |
1,176 | 2 | Title: Distributed Representations and Nested Compositional Structure
Abstract: Adaptation of ecological systems to their environments is commonly viewed through some explicit fitness function defined a priori by the experimenter, or measured a posteriori by estimations based on population size and/or reproductive ra... | [
254,
1123,
1285,
1354,
1592,
2272
] | Test |
1,177 | 5 | Title: An Efficient Subsumption Algorithm for Inductive Logic Programming
Abstract: In this paper we investigate the efficiency of - subsumption (` ), the basic provability relation in ILP. As D ` C is NP-complete even if we restrict ourselves to linked Horn clauses and fix C to contain only a small constant number o... | [
1180,
1519,
1620,
1627
] | Test |
1,178 | 1 | Title: Strongly Typed Genetic Programming
Abstract: BBN Technical Report #7866: Abstract Genetic programming is a powerful method for automatically generating computer programs via the process of natural selection [Koza 92]. However, it has the limitation known as "closure", i.e. that all the variables, constants, ar... | [
163,
854,
956,
995,
1034,
1099,
1230,
1231,
1232,
1362,
1476,
1688,
1690,
1736,
1737
] | Validation |
1,179 | 2 | Title: Even with Arbitrary Transfer Functions, RCC Cannot Compute Certain FSA
Abstract: Category: algorithms and architectures | recurrent networks. No part of this paper has been submitted elsewhere. Preference: poster. Abstract Existing proofs demonstrating the computational limitations of the Recurrent Cascade Cor... | [
946
] | Test |
1,180 | 5 | Title: Efficient Algorithms for -Subsumption
Abstract: subsumption is a decidable but incomplete approximation of logic implication, important to inductive logic programming and theorem proving. We show that by context based elimination of possible matches a certain superset of the determinate clauses can be tested f... | [
1177,
1620
] | Train |
1,181 | 6 | Title: Learning Sparse Perceptrons
Abstract: We introduce a new algorithm designed to learn sparse perceptrons over input representations which include high-order features. Our algorithm, which is based on a hypothesis-boosting method, is able to PAC-learn a relatively natural class of target concepts. Moreover, the ... | [
25,
569,
1431
] | Validation |
1,182 | 5 | Title: Overcoming the myopia of inductive learning algorithms with RELIEFF
Abstract: Current inductive machine learning algorithms typically use greedy search with limited looka-head. This prevents them to detect significant conditional dependencies between the attributes that describe training objects. Instead of my... | [
1010,
1073,
1569,
1578,
1684,
1726
] | Validation |
1,183 | 4 | Title: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
Abstract: This paper describes the MAXQ method for hierarchical reinforcement learning based on a hierarchical decomposition of the value function and derives conditions under which the MAXQ decomposition can represent the optimal v... | [
562,
738,
1193,
1202
] | Train |
1,184 | 1 | Title: Causality in Genetic Programming
Abstract: Causality relates changes in the structure of an object with the effects of such changes, that is changes in the properties or behavior of the object. This paper analyzes the concept of causality in Genetic Programming (GP) and suggests how it can be used in adapting ... | [
120,
141,
781,
844,
860,
1362,
1784,
2199
] | Validation |
1,185 | 6 | Title: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Abstract: Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We re... | [
1000,
1521
] | Train |
1,186 | 3 | Title: Rationality and Intelligence
Abstract: The long-term goal of our field is the creation and understanding of intelligence. Productive research in AI, both practical and theoretical, benefits from a notion of intelligence that is precise enough to allow the cumulative development of robust systems and general re... | [
492,
591,
1268,
1309
] | Validation |
1,187 | 6 | Title: Rationality and Intelligence
Abstract: Design and Evaluation of the RISE 1.0 Learning System Pedro Domingos pedrod@ics.uci.edu Technical Report 94-34 August 30, 1994 | [
426,
1234
] | Train |
1,188 | 2 | Title: In Estimating analogical similarity by dot-products of Holographic Reduced Representations.
Abstract: Models of analog retrieval require a computationally cheap method of estimating similarity between a probe and the candidates in a large pool of memory items. The vector dot-product operation would be ideal f... | [
1123,
1354
] | Train |
1,189 | 6 | Title: Figure 3: Average model size accepted from a ran-dom prefix-closed samples of various size, and
Abstract: that is based on Angluin's L fl algorithm. The algorithm maintains a model consistent with its past examples. When a new counterexample arrives it tries to extend the model in a minimal fashion. We conducted... | [
638,
1643,
1687
] | Train |
1,190 | 2 | Title: Analysis of the Convergence and Generalization of AA1
Abstract: that is based on Angluin's L fl algorithm. The algorithm maintains a model consistent with its past examples. When a new counterexample arrives it tries to extend the model in a minimal fashion. We conducted a set of experiments where random autom... | [
809,
1129,
1321
] | Train |
1,191 | 6 | Title: Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms
Abstract: The term "bias" is widely used|and with different meanings|in the fields of machine learning and statistics. This paper clarifies the uses of this term and shows how to measure and visualize the statistical ... | [
661,
692,
1053,
1290,
2423
] | Validation |
1,192 | 4 | Title: Roles of Macro-Actions in Accelerating Reinforcement Learning
Abstract: We analyze the use of built-in policies, or macro-actions, as a form of domain knowledge that can improve the speed and scaling of reinforcement learning algorithms. Such macro-actions are often used in robotics, and macro-operators are al... | [
321,
875,
2150,
2473
] | Train |
1,193 | 4 | Title: Reinforcement Learning with Hierarchies of Machines
Abstract: We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This allows for the use of prior knowledge to reduce the search space and pr... | [
1183
] | Train |
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