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|---|---|---|---|---|
194 | 2 | Title: Improving the Quality of Automatic DNA Sequence Assembly using Fluorescent Trace-Data Classifications
Abstract: Virtually all large-scale sequencing projects use automatic sequence-assembly programs to aid in the determination of DNA sequences. The computer-generated assemblies require substantial handediting ... | [
673
] | Train |
195 | 5 | Title: d d Techniques for Extracting Instruction Level Parallelism on MIMD Architectures
Abstract: Extensive research has been done on extracting parallelism from single instruction stream processors. This paper presents some results of our investigation into ways to modify MIMD architectures to allow them to extract... | [
196,
707,
735
] | Train |
196 | 5 | Title: d d MISC: A Multiple Instruction Stream Computer
Abstract: This paper describes a single chip Multiple Instruction Stream Computer (MISC) capable of extracting instruction level parallelism from a broad spectrum of programs. The MISC architecture uses multiple asynchronous processing elements to separate a pro... | [
195,
216,
707
] | Train |
197 | 4 | Title: Optimal Navigation in a Probibalistic World
Abstract: In this paper, we define and examine two versions of the bridge problem. The first variant of the bridge problem is a determistic model where the agent knows a superset of the transitions and a priori probabilities that those transitions are intact. In the ... | [
3,
295,
633,
749
] | Test |
198 | 2 | Title: EEG Signal Classification with Different Signal Representations for a large number of hidden units.
Abstract: If several mental states can be reliably distinguished by recognizing patterns in EEG, then a paralyzed person could communicate to a device like a wheelchair by composing sequencesof these mental sta... | [
655,
747
] | Train |
199 | 6 | Title: On Learning Conjunctions with Malicious Noise
Abstract: We show how to learn monomials in the presence of malicious noise, when the underlined distribution is a product distribution. We show that our results apply not only to product distributions but to a wide class of distributions. | [
591,
640
] | Test |
200 | 2 | Title: Sample Complexity for Learning Recurrent Perceptron Mappings
Abstract: Recurrent perceptron classifiers generalize the classical perceptron model. They take into account those correlations and dependences among input coordinates which arise from linear digital filtering. This paper provides tight bounds on sam... | [
536,
1464,
1891
] | Train |
201 | 2 | Title: Neural net architectures for temporal sequence processing
Abstract: I present a general taxonomy of neural net architectures for processing time-varying patterns. This taxonomy subsumes many existing architectures in the literature, and points to several promising architectures that have yet to be examined. An... | [
143,
350,
427,
1718,
1990
] | Test |
202 | 2 | Title: Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon
Abstract: DISLEX is an artificial neural network model of the mental lexicon. It was built to test com-putationally whether the lexicon could consist of separate feature maps for the different lexical modal... | [
72,
204,
427,
747,
771
] | Train |
203 | 2 | Title: Theory of Synaptic Plasticity in Visual Cortex
Abstract: | [
359,
747,
2499
] | Train |
204 | 2 | Title: Natural Language Processing with Subsymbolic Neural Networks
Abstract: | [
202,
274,
597,
741,
747,
1645,
1811,
2410,
2650
] | Train |
205 | 2 | Title: Beyond the Cognitive Map: Contributions to a Computational Neuroscience Theory of Rodent Navigation for the
Abstract: | [
427,
600,
745,
747
] | Validation |
206 | 2 | Title: NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical
Abstract: This paper briefly surveys some recent results relevant | [
536,
1028,
1042,
1488,
1490,
1891
] | Train |
207 | 2 | Title: LEARNING BY ERROR-DRIVEN DECOMPOSITION
Abstract: In this paper we describe a new selforganizing decomposition technique for learning high-dimensional mappings. Problem decomposition is performed in an error-driven manner, such that the resulting subtasks (patches) are equally well approximated. Our method comb... | [
688,
747,
1536,
1676
] | Test |
208 | 6 | Title: Feature Subset Selection as Search with Probabilistic Estimates
Abstract: Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabi... | [
88,
430,
635,
1270,
1569,
2343
] | Train |
209 | 1 | Title: 17 Massively Parallel Genetic Programming
Abstract: As the field of Genetic Programming (GP) matures and its breadth of application increases, the need for parallel implementations becomes absolutely necessary. The transputer-based system presented in the chapter by Koza and Andre ([11]) is one of the rare suc... | [
415,
2334,
2704
] | Test |
210 | 4 | Title: A Unified Analysis of Value-Function-Based Reinforcement-Learning Algorithms
Abstract: Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work ... | [
63,
148,
295,
552,
644,
738,
1459
] | Train |
211 | 3 | Title: Using Path Diagrams as a Structural Equation Modelling Tool
Abstract: Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing impr... | [
325,
645,
1527,
2076
] | Train |
212 | 2 | Title: Analyzing Hyperspectral Data with Independent Component Analysis
Abstract: Hyperspectral image sensors provide images with a large number of contiguous spectral channels per pixel and enable information about different materials within a pixel to be obtained. The problem of spectrally unmixing materials may be... | [
169,
570,
576
] | Train |
213 | 4 | Title: Incremental methods for computing bounds in partially observable Markov decision processes
Abstract: Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect observabil-ity. The control pro... | [
490,
492,
734
] | Validation |
214 | 2 | Title: Bayesian Non-linear Modelling for the Prediction Competition
Abstract: The 1993 energy prediction competition involved the prediction of a series of building energy loads from a series of environmental input variables. Non-linear regression using `neural networks' is a popular technique for such modeling tasks... | [
78,
157,
469,
587
] | Train |
215 | 0 | Title: Integration of Case-Based Reasoning and Neural Networks Approaches for Classification
Abstract: Several different approaches have been used to describe concepts for supervised learning tasks. In this paper we describe two approaches which are: prototype-based incremental neural networks and case-based reasonin... | [
66,
478,
696,
2061,
2380
] | Train |
216 | 5 | Title: d d Code Scheduling for Multiple Instruction Stream Architectures
Abstract: Extensive research has been done on extracting parallelism from single instruction stream processors. This paper presents our investigation into ways to modify MIMD architectures to allow them to extract the instruction level paralleli... | [
196,
735
] | Test |
217 | 2 | Title: A Scalable Performance Prediction Method for Parallel Neural Network Simulations
Abstract: A performance prediction method is presented for indicating the performance range of MIMD parallel processor systems for neural network simulations. The total execution time of a parallel application is modeled as the su... | [
685,
747,
2355
] | Train |
218 | 3 | Title: Learning Classification Trees
Abstract: Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This introduces Bayesian techniques for splitting, smoot... | [
378,
478,
1290
] | Train |
219 | 1 | Title: Issues in Evolutionary Robotics
Abstract: A version of this paper appears in: Proceedings of SAB92, the Second International Conference on Simulation of Adaptive Behaviour J.-A. Meyer, H. Roitblat, and S. Wilson, editors, MIT Press Bradford Books, Cambridge, MA, 1993. | [
38,
163,
402,
563,
712,
757,
846,
1325,
1404,
1689,
1738,
2563
] | Train |
220 | 4 | Title: Learning sorting and decision trees with POMDPs
Abstract: pomdps are general models of sequential decisions in which both actions and observations can be probabilistic. Many problems of interest can be formulated as pomdps, yet the use of pomdps has been limited by the lack of effective algorithms. Recently th... | [
45,
295,
490,
552
] | Train |
221 | 0 | Title: Abstract
Abstract: Given an arbitrary learning situation, it is difficult to determine the most appropriate learning strategy. The goal of this research is to provide a general representation and processing framework for introspective reasoning for strategy selection. The learning framework for an introspectiv... | [
629
] | Test |
222 | 0 | Title: Abstract
Abstract: We describe an ongoing project to develop an adaptive training system (ATS) that dynamically models a students learning processes and can provide specialized tutoring adapted to a students knowledge state and learning style. The student modeling component of the ATS, ML-Modeler, uses machine... | [
581,
643
] | Validation |
223 | 5 | Title: Automated model selection
Abstract: Many algorithms have parameters that should be set by the user. For most machine learning algorithms parameter setting is a non-trivial task that influence knowledge model returned by the algorithm. Parameter values are usually set approximately according to some characteris... | [
430,
686
] | Train |
224 | 5 | Title: on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples
Abstract: This paper presents a method for learning logic programs without explicit negative examples by exploiting an assumption of output completeness. A mode declaration is supplied for the target predicate an... | [
597,
1601,
1819
] | Train |
225 | 0 | Title: on Inductive Logic Programming (ILP-95) Inducing Logic Programs without Explicit Negative Examples
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 take a query,... | [
88,
686,
2428
] | Validation |
226 | 2 | Title: A Neuro-Fuzzy Approach to Agglomerative Clustering
Abstract: In this paper, we introduce a new agglomerative clustering algorithm in which each pattern cluster is represented by a collection of fuzzy hyperboxes. Initially, a number of such hyperboxes are calculated to represent the pattern samples. Then, the a... | [
617
] | Validation |
227 | 6 | Title: Induction of Oblique Decision Trees
Abstract: This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques, such as ID3 and its descendants, partition a set of points with axis-parallel hyper-planes. Our method, by contrast, attempts to fin... | [
142,
296,
378,
438,
478,
638,
823,
1318,
1547,
2319
] | Train |
228 | 1 | Title: Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
Abstract: This paper introduces ICET, a new algorithm for costsensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness f... | [
52,
119,
259,
323,
900,
1692,
2465,
2617
] | Validation |
229 | 2 | Title: Understanding Musical Sound with Forward Models and Physical Models
Abstract: This paper introduces ICET, a new algorithm for costsensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is th... | [
477,
747
] | Train |
230 | 2 | Title: Mathematical Programming in Neural Networks
Abstract: This paper highlights the role of mathematical programming, particularly linear programming, in training neural networks. A neural network description is given in terms of separating planes in the input space that suggests the use of linear programming for ... | [
142,
391,
406,
427,
520,
1283,
1284
] | Validation |
231 | 0 | Title: Understanding Creativity: A Case-Based Approach
Abstract: Dissatisfaction with existing standard case-based reasoning (CBR) systems has prompted us to investigate how we can make these systems more creative and, more broadly, what would it mean for them to be more creative. This paper discusses three research ... | [
30,
285,
486
] | Train |
232 | 2 | Title: Stochastic Decomposition of DNA Sequences Using Hidden Markov Models
Abstract: This work presents an application of a machine learning for characterizing an important property of natural DNA sequences compositional inhomogeneity. Compositional segments often correspond to meaningful biological units. Taking in... | [
14,
268,
613
] | Train |
233 | 2 | Title: A `SELF-REFERENTIAL' WEIGHT MATRIX
Abstract: Weight modifications in traditional neural nets are computed by hard-wired algorithms. Without exception, all previous weight change algorithms have many specific limitations. Is it (in principle) possible to overcome limitations of hard-wired algorithms by allowing... | [
595,
1990
] | Train |
234 | 2 | Title: Multiassociative Memory
Abstract: This paper discusses the problem of how to implement many-to-many, or multi-associative, mappings within connectionist models. Traditional symbolic approaches wield explicit representation of all alternatives via stored links, or implicitly through enumerative algorithms. Clas... | [
15,
747
] | Train |
235 | 2 | Title: Development of triadic neural circuits for visual image stabilization under eye movements
Abstract: Human visual systems maintain a stable internal representation of a scene even though the image on the retina is constantly changing because of eye movements. Such stabilization can theoretically be effected by ... | [
747
] | Validation |
236 | 0 | Title: Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome
Abstract: This paper tries to identify rules and factors that are predictive for the outcome of international conflict management attempts. We use C4.5, an advanced Machine Learning algorithm, for genera... | [
156,
430,
1270,
1271,
1617
] | Validation |
237 | 1 | Title: A Sequential Niche Technique for Multimodal Function Optimization
Abstract: c fl UWCC COMMA Technical Report No. 93001, February 1993 x No part of this article may be reproduced for commercial purposes. Abstract A technique is described which allows unimodal function optimization methods to be extended to effi... | [
163,
329,
1060
] | Test |
238 | 2 | Title: Learning from Examples, Agent Teams and the Concept of Reflection
Abstract: In International Journal of Pattern Recognition and AI, 10(3):251-272, 1996 Also available as GMD report #766 | [
46,
193,
301,
489,
1815
] | Validation |
239 | 4 | Title: Robust Value Function Approximation by Working Backwards Computing an accurate value function is the key
Abstract: In this paper, we examine the intuition that TD() is meant to operate by approximating asynchronous value iteration. We note that on the important class of discrete acyclic stochastic tasks, value i... | [
82,
552,
565,
1378
] | Validation |
240 | 1 | Title: A Transformation System for Interactive Reformulation of Design Optimization Strategies
Abstract: Automatic design optimization is highly sensitive to problem formulation. The choice of objective function, constraints and design parameters can dramatically impact the computational cost of optimization and the ... | [
61,
2308,
2652
] | Train |
241 | 2 | Title: Segmentation and Classification of Combined Optical and Radar Imagery
Abstract: The classification performance of a neural network for combined six-band Landsat-TM and one-band ERS-1/SAR PRI imagery from the same scene is carried out. Different combinations of the data | either raw, segmented or filtered |, us... | [
763
] | Train |
242 | 6 | Title: Learning Markov chains with variable memory length from noisy output
Abstract: The problem of modeling complicated data sequences, such as DNA or speech, often arises in practice. Most of the algorithms select a hypothesis from within a model class assuming that the observed sequence is the direct output of th... | [
14,
574,
1006
] | Train |
243 | 1 | Title: Distribution Category: Users Guide to the PGAPack Parallel Genetic Algorithm Library
Abstract: The problem of modeling complicated data sequences, such as DNA or speech, often arises in practice. Most of the algorithms select a hypothesis from within a model class assuming that the observed sequence is the di... | [
53,
357,
728
] | Validation |
244 | 2 | Title: Building Intelligent Agents for Web-Based Tasks: A Theory-Refinement Approach
Abstract: We present and evaluate an infrastructure with which to rapidly and easily build intelligent software agents for Web-based tasks. Our design is centered around two basic functions: ScoreThis-Link and ScoreThisPage. If given... | [
136,
565,
750
] | Validation |
245 | 0 | Title: ICML-96 Workshop "Learning in context-sensitive domains" Bari, Italy. Dynamically Adjusting Concepts to Accommodate Changing Contexts
Abstract: In concept learning, objects in a domain are grouped together based on similarity as determined by the attributes used to describe them. Existing concept learners requ... | [
172,
1636
] | Train |
246 | 3 | Title: Bayesian Mixture Modeling by Monte Carlo Simulation
Abstract: It is shown that Bayesian inference from data modeled by a mixture distribution can feasibly be performed via Monte Carlo simulation. This method exhibits the true Bayesian predictive distribution, implicitly integrating over the entire underlying p... | [
560
] | Test |
247 | 4 | Title: Machine Learning, Efficient Reinforcement Learning through Symbiotic Evolution
Abstract: This article presents a new reinforcement learning method called SANE (Symbiotic, Adaptive Neuro-Evolution), which evolves a population of neurons through genetic algorithms to form a neural network capable of performing ... | [
500,
563,
982,
1117,
1261,
1973,
2257,
2446
] | Train |
248 | 3 | Title: Probabilistic evaluation of sequential plans from causal models with hidden variables
Abstract: The paper concerns the probabilistic evaluation of plans in the presence of unmeasured variables, each plan consisting of several concurrent or sequential actions. We establish a graphical criterion for recognizing ... | [
105,
398,
419,
1326,
2088
] | Train |
249 | 5 | Title: Control Flow Prediction For Dynamic ILP Processors
Abstract: We introduce a technique to enhance the ability of dynamic ILP processors to exploit (speculatively executed) parallelism. Existing branch prediction mechanisms used to establish a dynamic window from which ILP can be extracted are limited in their a... | [
86,
652,
2649
] | Train |
250 | 3 | Title: Mean Field Theory for Sigmoid Belief Networks
Abstract: We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the like... | [
31,
33,
76,
107,
108,
170,
304,
427,
498,
499,
577,
584,
639,
694,
708,
736
] | Train |
251 | 6 | Title: A Statistical Approach to Solving the EBL Utility Problem
Abstract: Many "learning from experience" systems use information extracted from problem solving experiences to modify a performance element PE, forming a new element PE 0 that can solve these and similar problems more efficiently. However, as transform... | [
6,
88,
482,
865,
932,
1505,
1877,
2560
] | Train |
252 | 4 | Title: A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar
Abstract: Compositional Q-Learning (CQ-L) (Singh 1992) is a modular approach to learning to perform composite tasks made up of several elemental tasks by reinforcement learning. Skills acquired while per... | [
60,
74,
294,
562,
688
] | Train |
253 | 2 | Title: Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity
Abstract: The processing performed by a feed-forward neural network is often interpreted through use of decision hyperplanes at each layer. The adaptation process, however, is normally explained using the picture o... | [
15,
1815,
2670
] | Train |
254 | 2 | Title: Scaling-up RAAMs
Abstract: Modifications to Recursive Auto-Associative Memory are presented, which allow it to store deeper and more complex data structures than previously reported. These modifications include adding extra layers to the compressor and reconstructor networks, employing integer rather than real... | [
15,
1176
] | Train |
255 | 6 | Title: An Efficient Boosting Algorithm for Combining Preferences
Abstract: The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for... | [
70,
421,
569,
767
] | Validation |
256 | 0 | Title: Using Decision Trees to Improve Case-Based Learning
Abstract: This paper shows that decision trees can be used to improve the performance of case-based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-flexible prediction that lies between the classification task ... | [
430,
634,
635,
928,
983,
2225,
2369
] | Test |
257 | 2 | Title: Factor Analysis Using Delta-Rule Wake-Sleep Learning
Abstract: Technical Report No. 9607, Department of Statistics, University of Toronto We describe a linear network that models correlations between real-valued visible variables using one or more real-valued hidden variables a factor analysis model. This mode... | [
36,
480,
667
] | Test |
258 | 2 | Title: Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families
Abstract: A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein family or the columns of a multiple alignment of that family is introduced. This method uses Diri... | [
0,
7,
8,
14,
268,
400,
435,
544,
751,
1031,
1111
] | Train |
259 | 0 | Title: How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications
Abstract: This paper proposes a simple cost model for machine learning applications based on the notion of net present value. The model extends and unifies the models used in (Pazzani et al., 1994) and (Masand & Piatetsky-Shapiro, 1... | [
228,
320,
582
] | Train |
260 | 3 | Title: ASPECTS OF GRAPHICAL MODELS CONNECTED WITH CAUSALITY
Abstract: This paper demonstrates the use of graphs as a mathematical tool for expressing independenices, and as a formal language for communicating and processing causal information in statistical analysis. We show how complex information about external int... | [
419,
619,
1324,
1527,
2069,
2166,
2524,
2559
] | Train |
261 | 2 | Title: Soft Vector Quantization and the EM Algorithm Running Title: Soft Vector Quantization and EM Section:
Abstract: This paper demonstrates the use of graphs as a mathematical tool for expressing independenices, and as a formal language for communicating and processing causal information in statistical analysis. We ... | [
345,
626
] | Validation |
262 | 0 | Title: EVOLVING REPRESENTATIONS OF DESIGN CASES AND THEIR USE IN CREATIVE DESIGN
Abstract: In case-based design, the adaptation of a design case to new design requirements plays an important role. If it is sufficient to adapt a predefined set of design parameters, the task is easily automated. If, however, more far-r... | [
188,
266,
793,
1980
] | Train |
263 | 2 | Title: Non-linear Models for Time Series Using Mixtures of Experts
Abstract: We consider a novel non-linear model for time series analysis. The study of this model emphasizes both theoretical aspects as well as practical applicability. The architecture of the model is demonstrated to be sufficiently rich, in the sens... | [
74,
668,
2421
] | Validation |
264 | 6 | Title: On Learning More Concepts
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 previous upper bound on the c... | [
635,
638
] | Train |
265 | 1 | Title: Analyzing GAs Using Markov Models with Semantically Ordered and Lumped States
Abstract: At the previous FOGA workshop, we presented some initial results on using Markov models to analyze the transient behavior of genetic algorithms (GAs) being used as function optimizers (GAFOs). In that paper, the states of t... | [
100,
758
] | Train |
266 | 0 | Title: EMERGENT BEHAVIOUR IN CO-EVOLUTIONARY DESIGN
Abstract: An important aspect of creative design is the concept of emergence. Though emergence is important, its mechanism is either not well understood or it is limited to the domain of shapes. This deficiency can be compensated by considering definitions of emerge... | [
163,
262
] | Validation |
267 | 6 | Title: On Learning from Noisy and Incomplete Examples
Abstract: We investigate learnability in the PAC model when the data used for learning, attributes and labels, is either corrupted or incomplete. In order to prove our main results, we define a new complexity measure on statistical query (SQ) learning algorithms. ... | [
20,
334,
459,
732
] | Train |
268 | 2 | Title: Finding Genes in DNA with a Hidden Markov Model
Abstract: This study describes a new Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences into exons, introns, and intergenic regions. Separate HMM modules were designed and trained for specific regions of DNA: exons, introns, int... | [
14,
232,
258,
613,
616,
2046,
2571
] | Train |
269 | 2 | Title: Discovering Structure in Multiple Learning Tasks: The TC Algorithm
Abstract: Recently, there has been an increased interest in lifelong machine learning methods, that transfer knowledge across multiple learning tasks. Such methods have repeatedly been found to outperform conventional, single-task learning algo... | [
24
] | Validation |
270 | 3 | Title: Generalized Update: Belief Change in Dynamic Settings
Abstract: Belief revision and belief update have been proposed as two types of belief change serving different purposes. Belief revision is intended to capture changes of an agent's belief state reflecting new information about a static world. Belief update... | [
276,
339,
342,
467,
495,
573
] | Train |
271 | 2 | Title: Bayesian Regression Filters and the Issue of Priors
Abstract: We propose a Bayesian framework for regression problems, which covers areas which are usually dealt with by function approximation. An online learning algorithm is derived which solves regression problems with a Kalman filter. Its solution always im... | [
718,
2056
] | Train |
272 | 2 | Title: A Performance Analysis of CNS-1 on Sparse Connectionist Networks
Abstract: This report deals with the efficient mapping of sparse neural networks on CNS-1. We develop parallel vector code for an idealized sparse network and determine its performance under three memory systems. We use the code to evaluate the m... | [
516,
914,
1551
] | Train |
273 | 1 | Title: Two is better than one: A diploid genotype for neural networks
Abstract: In nature the genotype of many organisms exhibits diploidy, i.e., it includes two copies of every gene. In this paper we describe the results of simulations comparing the behavior of haploid and diploid populations of ecological neural ne... | [
38,
372
] | Test |
274 | 4 | Title: Some Experiments with a Hybrid Model for Learning Sequential Decision Making
Abstract: In nature the genotype of many organisms exhibits diploidy, i.e., it includes two copies of every gene. In this paper we describe the results of simulations comparing the behavior of haploid and diploid populations of ecolog... | [
204,
478,
566
] | Test |
275 | 3 | Title: Belief Revision: A Critique
Abstract: We examine carefully the rationale underlying the approaches to belief change taken in the literature, and highlight what we view as methodological problems. We argue that to study belief change carefully, we must be quite explicit about the "ontology" or scenario underlyi... | [
464
] | Test |
276 | 3 | Title: A Qualitative Markov Assumption and Its Implications for Belief Change
Abstract: The study of belief change has been an active area in philosophy and AI. In recent years, two special cases of belief change, belief revision and belief update, have been studied in detail. Roughly speaking, revision treats a surp... | [
270,
342,
467,
1993,
2115,
2546
] | Train |
277 | 4 | Title: Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local
Abstract: In this paper, we describe methods for efficiently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boost the power ... | [
294,
483,
523,
552,
567
] | Test |
278 | 3 | Title: Generalized Queries on Probabilistic Context-Free Grammars on Pattern Analysis and Machine Intelligence
Abstract: In this paper, we describe methods for efficiently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boost the power of... | [
324,
326,
1898
] | Train |
279 | 3 | Title: Qualitative Probabilities for Default Reasoning, Belief Revision, and Causal Modeling
Abstract: This paper presents recent developments toward a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery for ... | [
464
] | Train |
280 | 3 | Title: USING SMOOTHING SPLINE ANOVA TO EXAMINE THE RELATION OF RISK FACTORS TO THE INCIDENCE AND
Abstract: This paper presents recent developments toward a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery fo... | [
10,
192,
193,
519,
2549
] | Test |
281 | 4 | Title: Clay: Integrating Motor Schemas and Reinforcement Learning
Abstract: Clay is an evolutionary architecture for autonomous robots that integrates motor schema-based control and reinforcement learning. Robots utilizing Clay benefit from the real-time performance of motor schemas in continuous and dynamic environm... | [
460,
858
] | Test |
282 | 2 | Title: Cortical Synchronization and Perceptual Framing
Abstract: Clay is an evolutionary architecture for autonomous robots that integrates motor schema-based control and reinforcement learning. Robots utilizing Clay benefit from the real-time performance of motor schemas in continuous and dynamic environments while ... | [
589,
592
] | Test |
283 | 2 | Title: A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks
Abstract: Most known learning algorithms for dynamic neural networks in non-stationary environments need global computations to perform credit assignment. These algorithms either are not local in time or not local in space. Those algorit... | [
523,
565,
747,
2093
] | Train |
284 | 0 | Title: Role of Ontology in Creative Understanding
Abstract: In Proceedings of the 18th Annual Cognitive Science Conference, San Diego, CA, July 1996 This paper can also be found at the Georgia Tech WWW site: http://www.cc.gatech.edu/cogsci/ Abstract Successful creative understanding requires that a reasoner be able t... | [
64,
486,
583
] | Validation |
285 | 0 | Title: Explaining Serendipitous Recognition in Design
Abstract: Creative designers often see solutions to pending design problems in the everyday objects surrounding them. This can often lead to innovation and insight, sometimes revealing new functions and purposes for common design pieces in the process. We are inte... | [
30,
231,
486,
1148
] | Validation |
286 | 5 | Title: The Estimation of Probabilities in Attribute Selection Measures for Decision Tree Induction
Abstract: In this paper we analyze two well-known measures for attribute selection in decision tree induction, informativity and gini index. In particular, we are interested in the influence of different methods for est... | [
178,
378,
1963,
2195
] | Train |
287 | 6 | Title: Learning Switching Concepts
Abstract: We consider learning in situations where the function used to classify examples may switch back and forth between a small number of different concepts during the course of learning. We examine several models for such situations: oblivious models in which switches are made ... | [
549,
591,
640
] | Train |
288 | 0 | Title: A Formal Analysis of Case Base Retrieval
Abstract: Case based systems typically retrieve cases from the case base by applying similarity measures. The measures are usually constructed in an ad hoc manner. This report presents a toolbox for the systematic construction of similarity measures. In addition to pavi... | [
66,
75,
1377,
2037,
2157,
2380
] | Validation |
289 | 0 | Title: A theory of questions and question asking
Abstract: | [
64,
612,
1278,
1498,
1534,
1535,
1537,
2568
] | Train |
290 | 1 | Title: 20 Data Structures and Genetic Programming two techniques for reducing run time.
Abstract: In real world applications, software engineers recognise the use of memory must be organised via data structures and that software using the data must be independant of the data structures' implementation details. They ... | [
163,
1839,
1911,
2220
] | Validation |
291 | 2 | Title: NETWORKS WITH REAL WEIGHTS: ANALOG COMPUTATIONAL COMPLEXITY In contrast to classical computational models, the models
Abstract: Report SYCON-92-05 ABSTRACT We pursue a particular approach to analog computation, based on dynamical systems of the type used in neural networks research. Our systems have a fixed stru... | [
487
] | Test |
292 | 3 | Title: An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms
Abstract: We introduce a convergence diagnostic procedure for MCMC which operates by estimating total variation distances for the distribution of the algorithm after certain numbers of iterations. The method has advantages over many exist... | [
759,
904
] | Test |
293 | 2 | Title: Independent Component Analysis of Electroencephalographic Data
Abstract: Because of the distance between the skull and brain and their different resistivities, electroencephalographic (EEG) data collected from any point on the human scalp includes activity generated within a large brain area. This spatial smea... | [
387,
576
] | Validation |
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