node_id int64 0 76.9k | label int64 0 39 | text stringlengths 13 124k | neighbors listlengths 0 3.32k | mask stringclasses 4
values |
|---|---|---|---|---|
694 | 2 | Title: Localist Attractor Networks
Abstract: Attractor networks, which map a continuous input space to a discrete output space, are useful for pattern completion, cleaning up noisy or missing features in an input. However, designing a net to have a given set of attractors is notoriously tricky; training procedures ar... | [
76,
250
] | Test |
695 | 6 | Title: There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles
Abstract: According to Wolpert's no-free-lunch (NFL) theorems [1, 2], gener-alisation in the absence of domain knowledge is necessarily a zero-sum enterprise. Good generalisation performance in one situation is always offset ... | [
659,
747,
1967
] | Validation |
696 | 2 | Title: GAL: Networks that grow when they learn and shrink when they forget
Abstract: Learning when limited to modification of some parameters has a limited scope; the capability to modify the system structure is also needed to get a wider range of the learnable. In the case of artificial neural networks, learning by ... | [
215,
427,
579,
747,
1672
] | Test |
697 | 3 | Title: Estimating Dependency Structure as a Hidden Variable
Abstract: This paper introduces a probability model, the mixture of trees that can account for sparse, dynamically changing dependence relationships. We present a family of efficient algorithms that use EM and the Minimum Spanning Tree algorithm to find the ... | [
642
] | Validation |
698 | 2 | Title: Learning to Predict Reading Frames in E. coli DNA Sequences
Abstract: Two fundamental problems in analyzing DNA sequences are (1) locating the regions of a DNA sequence that encode proteins, and (2) determining the reading frame for each region. We investigate using artificial neural networks (ANNs) to find co... | [
360,
427,
474,
1431
] | Test |
699 | 4 | Title: Adaptive state space quantisation for reinforcement learning of collision-free navigation
Abstract: The paper describes a self-learning control system for a mobile robot. Based on sensor information the control system has to provide a steering signal in such a way that collisions are avoided. Since in our case... | [
294,
566,
588,
747
] | Test |
700 | 6 | Title: Is Transfer Inductive?
Abstract: Work is currently underway to devise learning methods which are better able to transfer knowledge from one task to another. The process of knowledge transfer is usually viewed as logically separate from the inductive procedures of ordinary learning. However, this paper argues t... | [
624,
625,
747,
879
] | Train |
701 | 2 | Title: Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and
Abstract: This chapter describes three studies which address the question of how neural network learning can be improved via the incorporation of information extracted from other networks. This gene... | [
15,
638,
1644
] | Train |
702 | 2 | Title: Automated Highway System
Abstract: ALVINN (Autonomous Land Vehicle in a Neural Net) is a Backpropagation trained neural network which is capable of autonomously steering a vehicle in road and highway environments. Although ALVINN is fairly robust, one of the problems with it has been the time it takes to train... | [
504
] | Train |
703 | 2 | Title: VECTOR ASSOCIATIVE MAPS: UNSUPERVISED REAL-TIME ERROR-BASED LEARNING AND CONTROL OF MOVEMENT TRAJECTORIES
Abstract: ALVINN (Autonomous Land Vehicle in a Neural Net) is a Backpropagation trained neural network which is capable of autonomously steering a vehicle in road and highway environments. Although ALVINN ... | [
747,
2233
] | Train |
704 | 3 | Title: EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCE APPROXIMATION FOR PREDICTIVE MODELING AND DATA MINING
Abstract: The work discussed in this paper is motivated by the need of building decision support systems for real-world problem domains. Our goal is to use these systems as a tool for supporting Bayes optimal ... | [
376
] | Train |
705 | 2 | Title: EXPERIMENTING WITH THE CHEESEMAN-STUTZ EVIDENCE APPROXIMATION FOR PREDICTIVE MODELING AND DATA MINING
Abstract: TECHNICAL REPORT NO. 947 June 5, 1995 | [
192,
519,
1910
] | Test |
706 | 6 | Title: Large Margin Classification Using the Perceptron Algorithm
Abstract: We introduce and analyze a new algorithm for linear classification which combines Rosenblatt's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik's maximal-margin classifier, our algorithm takes advantage of da... | [
453
] | Train |
707 | 5 | Title: Converting Thread-Level Parallelism to Instruction-Level Parallelism via Simultaneous Multithreading
Abstract: A version of this paper will appear in ACM Transactions on Computer Systems, August 1997. Permission to make digital copies of part or all of this work for personal or classroom use is grantedwithout ... | [
158,
195,
196
] | Validation |
708 | 2 | Title: GIBBS-MARKOV MODELS
Abstract: In this paper we present a framework for building probabilistic automata parameterized by context-dependent probabilities. Gibbs distributions are used to model state transitions and output generation, and parameter estimation is carried out using an EM algorithm where the M-step ... | [
14,
250,
1116
] | Train |
709 | 2 | Title: Convergence and new operations in SDM new method for converging in the SDM memory, utilizing
Abstract: Report R95:13 ISRN : SICS-R--95/13-SE ISSN : 0283-3638 Abstract | [
340,
341,
529
] | Test |
710 | 6 | Title: Improving Regressors using Boosting Techniques
Abstract: In the regression context, boosting and bagging are techniques to build a committee of regressors that may be superior to a single regressor. We use regression trees as fundamental building blocks in bagging committee machines and boosting committee mach... | [
438,
569,
847
] | Train |
711 | 3 | Title: NEULA: A hybrid neural-symbolic expert system shell
Abstract: Current expert systems cannot properly handle imprecise and incomplete information. On the other hand, neural networks can perform pattern recognition operations even in noisy environments. Against this background, we have implemented a neural exper... | [
485
] | Train |
712 | 1 | Title: Tracking the red queen: Measurements of adaptive progress in co-evolution ary simulations. In Third European
Abstract: Current expert systems cannot properly handle imprecise and incomplete information. On the other hand, neural networks can perform pattern recognition operations even in noisy environments. Agai... | [
54,
219,
415,
681,
1036,
1965,
2664
] | Train |
713 | 3 | Title: FLEXIBLE PARAMETRIC MEASUREMENT ERROR MODELS
Abstract: Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect then the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric... | [
161,
347
] | Train |
714 | 1 | Title: Orgy in the Computer: Multi-Parent Reproduction in Genetic Algorithms
Abstract: In this paper we investigate the phenomenon of multi-parent reproduction, i.e. we study recombination mechanisms where an arbitrary n > 1 number of parents participate in creating children. In particular, we discuss scanning cro... | [
163,
833,
1035,
1218,
1299,
2089
] | Test |
715 | 3 | Title: Covariate Selection in Hierarchical Models of Hospital Admission Counts: A Bayes Factor Approach 1
Abstract: TECHNICAL REPORT No. 268 Department of Statistics, GN-22 University of Washington Seattle, Washington 98195 USA 1 Susan L. Rosenkranz is Pew Health Policy Postdoctoral Fellow at the Institute for Health... | [
84
] | Train |
716 | 2 | Title: Covariate Selection in Hierarchical Models of Hospital Admission Counts: A Bayes Factor Approach 1
Abstract: Draft A Brief Introduction to Neural Networks Richard D. De Veaux Lyle H. Ungar Williams College University of Pennsylvania Abstract Artificial neural networks are being used with increasing frequency f... | [
157,
611
] | Train |
717 | 1 | Title: Information filtering: Selection mechanisms in learning systems. Machine Learning, 10:113-151, 1993. Generalization as search. Artificial
Abstract: Draft A Brief Introduction to Neural Networks Richard D. De Veaux Lyle H. Ungar Williams College University of Pennsylvania Abstract Artificial neural networks are b... | [
163,
188,
523,
1122,
1877,
2502,
2568
] | Train |
718 | 2 | Title: Gaussian Regression and Optimal Finite Dimensional Linear Models
Abstract: Draft A Brief Introduction to Neural Networks Richard D. De Veaux Lyle H. Ungar Williams College University of Pennsylvania Abstract Artificial neural networks are being used with increasing frequency for high dimensional problems of re... | [
271,
611
] | Train |
719 | 2 | Title: Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962.
Abstract: To apply the algorithm for classification we assign each class a separate set of codebook Gaussians. Each set is only trained with patterns from a single class. After having trained the... | [
87,
520,
609,
611,
747,
1112,
1133,
1666,
1838,
2561
] | Train |
720 | 2 | Title: Predicting Lifetimes in Dynamically Allocated Memory
Abstract: Predictions of lifetimes of dynamically allocated objects can be used to improve time and space efficiency of dynamic memory management in computer programs. Barrett and Zorn [1993] used a simple lifetime predictor and demonstrated this improvement... | [
438
] | Train |
721 | 1 | Title: Learning Representations for Evolutionary Computation an example from the domain of two-dimensional shape designs. In
Abstract: Evolutionary systems have been used in a variety of applications, from turbine design to scheduling problems. The basic algorithms are similar in all these applications, but the represe... | [
188
] | Train |
722 | 6 | Title: A Study of Maximal-Coverage Learning Algorithms
Abstract: The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previou... | [
635,
638
] | Train |
723 | 3 | Title: Exploiting Structure in Policy Construction
Abstract: Markov decision processes (MDPs) have recently been applied to the problem of modeling decision-theoretic planning. While traditional methods for solving MDPs are often practical for small states spaces, their effectiveness for large AI planning problems is... | [
552
] | Train |
724 | 2 | Title: ASOCS: A Multilayered Connectionist Network with Guaranteed Learning of Arbitrary Mappings
Abstract: This paper reviews features of a new class of multilayer connectionist architectures known as ASOCS (Adaptive Self-Organizing Concurrent Systems). ASOCS is similar to most decision-making neural network models ... | [
747,
2612
] | Train |
725 | 3 | Title: Maximum Working Likelihood Inference with Markov Chain Monte Carlo
Abstract: Maximum working likelihood (MWL) inference in the presence of missing data can be quite challenging because of the intractability of the associated marginal likelihood. This problem can be further exacerbated when the number of parame... | [
41,
48,
93
] | Train |
726 | 2 | Title: Natural image statistics and efficient coding
Abstract: Natural images contain characteristic statistical regularities that set them apart from purely random images. Understanding what these regularities are can enable natural images to be coded more efficiently. In this paper, we describe some of the forms of... | [
576,
1068,
1418
] | Train |
727 | 1 | Title: Using Problem Generators to Explore the Effects of Epistasis
Abstract: In this paper we develop an empirical methodology for studying the behavior of evolutionary algorithms based on problem generators. We then describe three generators that can be used to study the effects of epistasis on the performance of E... | [
163,
1016,
1136,
1799
] | Train |
728 | 1 | Title: On the Virtues of Parameterized Uniform Crossover
Abstract: Traditionally, genetic algorithms have relied upon 1 and 2-point crossover operators. Many recent empirical studies, however, have shown the benefits of higher numbers of crossover points. Some of the most intriguing recent work has focused on uniform... | [
243,
943,
1016,
1127,
1305,
1466
] | Train |
729 | 3 | Title: On the Complexity of Conditional Logics
Abstract: Conditional logics, introduced by Lewis and Stalnaker, have been utilized in artificial intelligence to capture a broad range of phenomena. In this paper we examine the complexity of several variants discussed in the literature. We show that, in general, decidi... | [
67,
342,
467,
1945,
1993
] | Validation |
730 | 2 | Title: Learning Sequential Tasks by Incrementally Adding Higher Orders
Abstract: An incremental, higher-order, non-recurrent network combines two properties found to be useful for learning sequential tasks: higher-order connections and incremental introduction of new units. The network adds higher orders when needed ... | [
350,
351,
770,
1889
] | Test |
731 | 2 | Title: LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)
Abstract: I propose a novel general principle for unsupervised learning of distributed non-redundant internal representations of input patterns. The principle is based on two opposing forces. For each representatio... | [
121,
330,
576,
808,
1450,
1656,
1778
] | Train |
732 | 6 | Title: Statistical Queries and Faulty PAC Oracles
Abstract: In this paper we study learning in the PAC model of Valiant [18] in which the example oracle used for learning may be faulty in one of two ways: either by misclassifying the example or by distorting the distribution of examples. We first consider models in w... | [
20,
267,
640,
1897
] | Test |
733 | 4 | Title: Evolutionary Differentiation of Learning Abilities a case study on optimizing parameter values in Q-learning by
Abstract: This paper describes the first stage of our study on evolution of learning abilities. We use a simple maze exploration problem designed by R. Sut-ton as the task of each individual, and encod... | [
566
] | Train |
734 | 4 | Title: Efficient dynamic-programming updates in partially observable Markov decision processes
Abstract: We examine the problem of performing exact dynamic-programming updates in partially observable Markov decision processes (pomdps) from a computational complexity viewpoint. Dynamic-programming updates are a crucia... | [
213,
490,
492
] | Validation |
735 | 5 | Title: The Limits of Instruction Level Parallelism in SPEC95 Applications
Abstract: This paper examines the limits to instruction level parallelism that can be found in programs, in particular the SPEC95 benchmark suite. It differs from earlier studies in removing non-essential true dependencies that occur as a resul... | [
86,
195,
216,
307,
1956,
2106,
2527,
2649
] | Train |
736 | 2 | Title: GIBBS-MARKOV MODELS
Abstract: In this paper we present a framework for building probabilistic automata parameterized by context-dependent probabilities. Gibbs distributions are used to model state transitions and output generation, and parameter estimation is carried out using an EM algorithm where the M-step ... | [
14,
250,
1116
] | Test |
737 | 2 | Title: The Role of Constraints in Hebbian Learning
Abstract: Models of unsupervised correlation-based (Hebbian) synaptic plasticity are typically unstable: either all synapses grow until each reaches the maximum allowed strength, or all synapses decay to zero strength. A common method of avoiding these outcomes is to... | [
747,
2024
] | Train |
738 | 4 | Title: On the Convergence of Stochastic Iterative Dynamic Programming Algorithms
Abstract: Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD() algorithm of Sutton (1988) and the... | [
210,
552,
564,
565,
575,
621,
1183,
1213,
1376,
1585,
1727,
1741,
2628,
2629
] | Test |
739 | 4 | Title: On the Convergence of Stochastic Iterative Dynamic Programming Algorithms
Abstract: Empirical Comparison of Gradient Descent and Exponentiated Gradient Descent in Supervised and Reinforcement Learning Technical Report 96-70 | [
63
] | Test |
740 | 6 | Title: Information-based objective functions for active data selection
Abstract: Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed which measure the expected informativeness of candidate measurements.... | [
157,
164,
418,
560,
929,
1559,
1664,
1667,
1683,
1703
] | Train |
741 | 2 | Title: Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map
Abstract: Knowledge of clusters and their relations is important in understanding high-dimensional input data with unknown distribution. Ordinary feature maps with fully connected, fixed grid topology cannot proper... | [
204,
687,
747,
1157
] | Test |
742 | 3 | Title: A Graphical Characterization of Lattice Conditional Independence Models
Abstract: Lattice conditional independence (LCI) models for multivariate normal data recently have been introduced for the analysis of non-monotone missing data patterns and of nonnested dependent linear regression models ( seemingly unrel... | [
645,
772,
1240
] | Validation |
743 | 1 | Title: Learning to be Selective in Genetic-Algorithm-Based Design Optimization
Abstract: Lattice conditional independence (LCI) models for multivariate normal data recently have been introduced for the analysis of non-monotone missing data patterns and of nonnested dependent linear regression models ( seemingly unrel... | [
65,
163,
744,
2030,
2077,
2316,
2659
] | Train |
744 | 1 | Title: A Genetic Algorithm for Continuous Design Space Search
Abstract: Genetic algorithms (GAs) have been extensively used as a means for performing global optimization in a simple yet reliable manner. However, in some realistic engineering design optimization domains the simple, classical implementation of a GA bas... | [
65,
163,
743,
2030,
2077,
2316
] | Train |
745 | 2 | Title: References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature
Abstract: 2] D. E. Rumelhart, G. E. Hinton and R. J. Williams, "Learning Internal Representations by Error Propagation", in D. E. Rumelhart and J. L. McClelland (eds.) Parallel Distributed Pro... | [
18,
36,
72,
91,
110,
124,
127,
205,
355,
386,
458,
477,
561,
600,
666,
687,
771,
962,
1157,
1564,
1700,
1704,
1756,
1763,
1884,
1885,
1886,
1932,
2162,
2165,
2325,
2336,
2437
] | Train |
746 | 2 | Title: A New Look at Tree Models for Multiple Sequence Alignment
Abstract: Evolutionary trees are frequently used as the underlying model in the design of algorithms, optimization criteria and software packages for multiple sequence alignment (MSA). In this paper, we reexamine the suitability of trees as a universal ... | [
14,
299
] | Train |
747 | 2 | Title: Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex.
Abstract: Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward... | [
18,
26,
43,
72,
73,
83,
91,
104,
110,
112,
113,
124,
127,
146,
153,
175,
186,
198,
202,
203,
204,
205,
207,
217,
229,
234,
235,
283,
310,
315,
328,
330,
336,
349,
353,
362,
368,
369,
386,
397,
458,
489,
494,
502,
526,
527,
5... | Validation |
748 | 3 | Title: Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function
Abstract: Technical Report No. 9722, Department of Statistics, University of Toronto Abstract. One way to sample from a distribution is to sample uniformly from the region under the plot of its density function. A Markov chain that conver... | [
137,
138,
1926,
1933,
1941
] | Train |
749 | 4 | Title: On the Complexity of Solving Markov Decision Problems
Abstract: Markov decision problems (MDPs) provide the foundations for a number of problems of interest to AI researchers studying automated planning and reinforcement learning. In this paper, we summarize results regarding the complexity of solving MDPs and... | [
197,
483,
552,
1459,
2485
] | Test |
750 | 4 | Title: Machine Learning, Creating Advice-Taking Reinforcement Learners
Abstract: Learning from reinforcements is a promising approach for creating intelligent agents. However, reinforcement learning usually requires a large number of training episodes. We present and evaluate a design that addresses this shortcoming... | [
244
] | Train |
751 | 2 | Title: Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology
Abstract: UCSC Technical Report UCSC-CRL-96-09 Abstract This paper presents the mathematical foundations of Dirichlet mixtures, which have been used to improve database search results for homologous sequences... | [
8,
14,
258,
435,
544
] | Validation |
752 | 0 | Title: Analysis and Empirical Studies of Derivational Analogy
Abstract: Derivational analogy is a technique for reusing problem solving experience to improve problem solving performance. This research addresses an issue common to all problem solvers that use derivational analogy: overcoming the mismatches between pas... | [
649,
1621
] | Validation |
753 | 2 | Title: Analysis of Dynamical Recognizers
Abstract: Pollack (1991) demonstrated that second-order recurrent neural networks can act as dynamical recognizers for formal languages when trained on positive and negative examples, and observed both phase transitions in learning and IFS-like fractal state sets. Follow-on wo... | [
405,
409,
444
] | Test |
754 | 2 | Title: Linear Machine Decision Trees
Abstract: COINS Technical Report 91-10 January 1991 Abstract This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlle... | [
478
] | Test |
755 | 1 | Title: of a simulator for evolving morphology are: Universal the simulator should cover an infinite gen
Abstract: Funes, P. and Pollack, J. (1997) Computer Evolution of Buildable Objects. Fourth European Conference on Artificial Life. P. Husbands and I. Harvey, eds., MIT Press. pp 358-367. knowledge into the program, w... | [
188,
757,
1404,
2058
] | Test |
756 | 5 | Title: Knowledge Acquisition via Knowledge Integration
Abstract: In this paper we are concerned with the problem of acquiring knowledge by integration. Our aim is to construct an integrated knowledge base from several separate sources. The need to merge knowledge bases can arise, for example, when knowledge bases are... | [
176,
379
] | Test |
757 | 1 | Title: Evolving Self-Supporting Structures Page 18 References Evolution of Visual Control Systems for Robots. To appear
Abstract: In this paper we are concerned with the problem of acquiring knowledge by integration. Our aim is to construct an integrated knowledge base from several separate sources. The need to merge k... | [
163,
188,
219,
755,
846,
1965,
2058
] | Train |
758 | 1 | Title: A COMPRESSION ALGORITHM FOR PROBABILITY TRANSITION MATRICES
Abstract: This paper describes a compression algorithm for probability transition matrices. The compressed matrix is itself a probability transition matrix. In general the compression is not error-free, but the error appears to be small even for high ... | [
100,
265,
1980
] | Train |
759 | 3 | Title: BAYESIAN STATISTICS 6, pp. 000--000 Exact sampling for Bayesian inference: towards general purpose algorithms
Abstract: There are now methods for organising a Markov chain Monte Carlo simulation so that it can be guaranteed that the state of the process at a given time is exactly drawn from the target distrib... | [
23,
93,
95,
99,
161,
292
] | Train |
760 | 0 | Title: BAYESIAN STATISTICS 6, pp. 000--000 Exact sampling for Bayesian inference: towards general purpose algorithms
Abstract: Instance-based learning methods explicitly remember all the data that they receive. They usually have no training phase, and only at prediction time do they perform computation. Then, they t... | [
88,
686,
2428
] | Train |
761 | 6 | Title: Apple Tasting and Nearly One-Sided Learning
Abstract: In the standard on-line model the learning algorithm tries to minimize the total number of mistakes made in a series of trials. On each trial the learner sees an instance, either accepts or rejects that instance, and then is told the appropriate response. W... | [
9,
40,
535
] | Validation |
762 | 6 | Title: Using Errors to Create Piecewise Learnable Partitions
Abstract: In this paper we describe an algorithm which exploits the error distribution generated by a learning algorithm in order to break up the domain which is being approximated into piecewise learnable partitions. Traditionally, the error distribution h... | [
88
] | Train |
763 | 2 | Title: PREENS, a Parallel Research Execution Environment for Neural Systems
Abstract: PREENS a Parallel Research Execution Environment for Neural Systems is a distributed neurosimulator, targeted on networks of workstations and transputer systems. As current applications of neural networks often contain large amounts... | [
241,
747,
1879,
2355
] | Test |
764 | 1 | Title: GENETIC AND NON GENETIC OPERATORS IN ALECSYS
Abstract: It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficult to regulate interplay between the reward system and the background genetic algorithm (GA), rule ... | [
636,
769,
910,
1311,
1573,
1581,
2174,
2687
] | Train |
765 | 1 | Title: A Classifier System plays a simple board game Getting down to the Basics of Machine Learning?
Abstract: It is well known that standard learning classifier systems, when applied to many different domains, exhibit a number of problems: payoff oscillation, difficult to regulate interplay between the reward system... | [
163
] | Validation |
766 | 2 | Title: Keeping Neural Networks Simple by Minimizing the Description Length of the Weights
Abstract: Supervised neural networks generalize well if there is much less information in the weights than there is in the output vectors of the training cases. So during learning, it is important to keep the weights simple by p... | [
78,
157,
181,
518,
979,
2532
] | Train |
767 | 6 | Title: Learning to Order Things
Abstract: There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i.e., statements to the effect that one instance should be ranked ahead of ... | [
255,
569
] | Test |
768 | 3 | Title: DYNAMIC CONDITIONAL INDEPENDENCE MODELS AND MARKOV CHAIN MONTE CARLO METHODS
Abstract: There are many applications in which it is desirable to order rather than classify instances. Here we consider the problem of learning how to order, given feedback in the form of preference judgments, i.e., statements to the... | [
772
] | Train |
769 | 1 | Title: On the Relations Between Search and Evolutionary Algorithms
Abstract: Technical Report: CSRP-96-7 March 1996 Abstract Evolutionary algorithms are powerful techniques for optimisation whose operation principles are inspired by natural selection and genetics. In this paper we discuss the relation between evoluti... | [
163,
764
] | Train |
770 | 2 | Title: A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages
Abstract: We present a neural net architecture that can discover hierarchical and recursive structure in symbol strings. To detect structure at multiple levels, the architecture has the capability of reducing symbols subs... | [
350,
730,
1285
] | Test |
771 | 2 | Title: SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION
Abstract: Self-organizing feature maps are usually implemented by abstracting the low-level neural and parallel distributed processes. An external supervisor finds the unit whose weight vector is closest in Euclidian dista... | [
72,
104,
202,
745,
747
] | Validation |
772 | 3 | Title: [12] J. Whittaker. Graphical Models in Applied Mathematical Multivariate Statis-
Abstract: Self-organizing feature maps are usually implemented by abstracting the low-level neural and parallel distributed processes. An external supervisor finds the unit whose weight vector is closest in Euclidian distance to t... | [
51,
312,
742,
768,
1147,
1240,
1241,
1502,
2076,
2166,
2167
] | Train |
773 | 4 | Title: Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems
Abstract: The application of decision making and learning algorithms to multi-agent systems presents many interestingresearch challenges and opportunities. Among these is the ability for agents to learn how to act by observing or imitat... | [
565,
656,
1643,
1687
] | Train |
774 | 2 | Title: Face Recognition: A Hybrid Neural Network Approach
Abstract: Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult (Turk and Pentland, 1991). We present a hybrid neural network solution which compares favorably with other met... | [
331,
1493,
2707
] | Test |
775 | 4 | Title: Asynchronous Modified Policy Iteration with Single-sided Updates
Abstract: We present a new algorithm for solving Markov decision problems that extends the modified policy iteration algorithm of Puterman and Shin [6] in two important ways: 1) The new algorithm is asynchronous in that it allows the values of st... | [
162,
875,
1459
] | Train |
776 | 3 | Title: CAUSATION, ACTION, AND COUNTERFACTUALS
Abstract: We present a new algorithm for solving Markov decision problems that extends the modified policy iteration algorithm of Puterman and Shin [6] in two important ways: 1) The new algorithm is asynchronous in that it allows the values of states to be updated in arbi... | [
342,
398,
1326,
2088,
2166
] | Train |
777 | 3 | Title: MARKOV CHAIN MONTE CARLO SAMPLING FOR EVALUATING MULTIDIMENSIONAL INTEGRALS WITH APPLICATION TO BAYESIAN COMPUTATION
Abstract: Recently, Markov chain Monte Carlo (MCMC) sampling methods have become widely used for determining properties of a posterior distribution. Alternative to the Gibbs sampler, we elaborat... | [
533
] | Validation |
778 | 6 | Title: On the Sample Complexity of Noise-Tolerant Learning
Abstract: In this paper, we further characterize the complexity of noise-tolerant learning in the PAC model. Specifically, we show a general lower bound of log(1=ffi) on the number of examples required for PAC learning in the presence of classification noise.... | [
25,
56,
109
] | Train |
779 | 2 | Title: Monte Carlo Comparison of Non-hierarchical Unsupervised Classifiers
Abstract: In this paper, we further characterize the complexity of noise-tolerant learning in the PAC model. Specifically, we show a general lower bound of log(1=ffi) on the number of examples required for PAC learning in the presence of class... | [
542,
638,
684,
747,
1203
] | Validation |
780 | 1 | Title: Between-host evolution of mutation-rate and within-host evolution of virulence.
Abstract: It has been recently realized that parasite virulence (the harm caused by parasites to their hosts) can be an adaptive trait. Selection for a particular level of virulence can happen either at at the level of between-host... | [
1139,
1598
] | Test |
781 | 1 | Title: Evolving Visual Routines Architecture and Planning,
Abstract: It has been recently realized that parasite virulence (the harm caused by parasites to their hosts) can be an adaptive trait. Selection for a particular level of virulence can happen either at at the level of between-host tradeoffs or as a result o... | [
163,
846,
1184,
1533
] | Train |
782 | 2 | Title: on Qualitative Reasoning about Physical Systems Deriving Monotonic Function Envelopes from Observations
Abstract: Much work in qualitative physics involves constructing models of physical systems using functional descriptions such as "flow monotonically increases with pressure." Semiquantitative methods impro... | [
1532
] | Train |
783 | 0 | Title: Resolving PP attachment Ambiguities with Memory-Based Learning
Abstract: In this paper we describe the application of Memory-Based Learning to the problem of Prepositional Phrase attachment disambiguation. We compare Memory-Based Learning, which stores examples in memory and generalizes by using intelligent si... | [
1155,
1328,
1407,
1812
] | Validation |
784 | 2 | Title: Studies of Neurological Transmission Analysis using Hierarchical Bayesian Mixture Models
Abstract: Hierarchically structured mixture models are studied in the context of data analysis and inference on neural synaptic transmission characteristics in mammalian, and other, central nervous systems. Mixture structu... | [
845,
917,
1338,
1613
] | Validation |
785 | 0 | Title: RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING
Abstract: The need for software modules performing natural language processing (NLP) tasks is growing. These modules should perform efficiently and accurately, while at the same time rapid development is often mandatory. Recent work has indicated that... | [
862,
1155,
1328,
1513,
1812
] | Train |
786 | 6 | Title: Learning Boolean Read-Once Formulas over Generalized Bases
Abstract: A read-once formula is one in which each variable appears on at most a single input. Angluin, Hellerstein, and Karpinski give a polynomial time algorithm that uses membership and equivalence queries to identify exactly read-once boolean formu... | [
791,
1003,
1004,
1363
] | Test |
787 | 3 | Title: Hidden Markov decision trees
Abstract: We study a time series model that can be viewed as a decision tree with Markov temporal structure. The model is intractable for exact calculations, thus we utilize variational approximations. We consider three different distributions for the approximation: one in which th... | [
74,
1287,
1288,
1437
] | Validation |
788 | 3 | Title: Stochastic simulation algorithms for dynamic probabilistic networks
Abstract: Stochastic simulation algorithms such as likelihood weighting often give fast, accurate approximations to posterior probabilities in probabilistic networks, and are the methods of choice for very large networks. Unfortunately, the sp... | [
945,
1268,
1414,
2323,
2341,
2425
] | Train |
789 | 1 | Title: Stochastic Random or probabilistic but with some direction. For example the arrival of people at
Abstract: Simulated Annealing Search technique where a single trial solution is modified at random. An energy is defined which represents how good the solution is. The goal is to find the best solution by minimising ... | [
415,
1206,
1409
] | Train |
790 | 6 | Title: Learning with Rare Cases and Small Disjuncts
Abstract: Systems that learn from examples often create a disjunctive concept definition. Small disjuncts are those disjuncts which cover only a few training examples. The problem with small disjuncts is that they are more error prone than large disjuncts. This pape... | [
1234,
1510,
2057
] | Validation |
791 | 6 | Title: Asking Questions to Minimize Errors
Abstract: A number of efficient learning algorithms achieve exact identification of an unknown function from some class using membership and equivalence queries. Using a standard transformation such algorithms can easily be converted to on-line learning algorithms that use m... | [
786,
1003,
1004,
1560,
1661
] | Train |
792 | 6 | Title: Learning Unions of Rectangles with Queries
Abstract: A number of efficient learning algorithms achieve exact identification of an unknown function from some class using membership and equivalence queries. Using a standard transformation such algorithms can easily be converted to on-line learning algorithms tha... | [
798,
1095
] | Train |
793 | 1 | Title: A Survey of Evolution Strategies
Abstract: | [
42,
163,
262,
856,
943,
959,
1069,
1070,
1110,
1127,
1139,
1205,
1249,
1330,
1333,
1334,
1380,
1455,
1467,
1691,
1694,
1715,
1734
] | Train |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.