node_id int64 0 76.9k | label int64 0 39 | text stringlengths 13 124k | neighbors listlengths 0 3.32k | mask stringclasses 4
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|---|---|---|---|---|
2,194 | 5 | Title: Minimizing Register Requirements under Resource-Constrained Rate-Optimal Software Pipelining
Abstract: In this paper we address the following software pipelin-ing problem: given a loop and a machine architecture with a fixed number of processor resources (e.g. function units), how can one construct a software-... | [
1955,
2149,
2188,
2189,
2190
] | Validation |
2,195 | 5 | Title: LEARNING FOR DECISION MAKING: The FRD Approach and a Comparative Study Machine Learning and Inference Laboratory
Abstract: This paper concerns the issue of what is the best form for learning, representing and using knowledge for decision making. The proposed answer is that such knowledge should be learned and... | [
286,
378,
1963
] | Test |
2,196 | 1 | Title: Effects of Occam's Razor in Evolving Sigma-Pi Neural Nets
Abstract: Several evolutionary algorithms make use of hierarchical representations of variable size rather than linear strings of fixed length. Variable complexity of the structures provides an additional representational power which may widen the appli... | [
163,
380,
938,
2267
] | Train |
2,197 | 6 | Title: MLC Tutorial A Machine Learning library of C classes.
Abstract: Several evolutionary algorithms make use of hierarchical representations of variable size rather than linear strings of fixed length. Variable complexity of the structures provides an additional representational power which may widen the applicati... | [
430,
2342
] | Test |
2,198 | 6 | Title: An Incremental Interactive Algorithm for Regular Grammar Inference
Abstract: We present provably correct interactive algorithms for learning regular grammars from positive examples and membership queries. A structurally complete set of strings from a language L(G) corresponding to a target regular grammar G im... | [
1560,
2537,
2695
] | Validation |
2,199 | 1 | Title: Position Paper, Workshop on Evolutionary Computation with Variable Size Representation, ICGA, Fitness Causes Bloat in
Abstract: We argue based upon the numbers of representations of given length, that increase in representation length is inherent in using a fixed evaluation function with a discrete but variable ... | [
1184,
1784,
2133
] | Train |
2,200 | 1 | Title: Adaptation in constant utility non-stationary environments
Abstract: Environments that vary over time present a fundamental problem to adaptive systems. Although in the worst case there is no hope of effective adaptation, some forms environmental variability do provide adaptive opportunities. We consider a bro... | [
163,
1797,
1969,
2703
] | Train |
2,201 | 2 | Title: Neural competitive maps for reactive and adaptive navigation
Abstract: We have recently introduced a neural network for reactive obstacle avoidance based on a model of classical and operant conditioning. In this article we describe the success of this model when implemented on two real autonomous robots. Our r... | [
2233
] | Train |
2,202 | 1 | Title: An Evolutionary Approach to Combinatorial Optimization Problems
Abstract: The paper reports on the application of genetic algorithms, probabilistic search algorithms based on the model of organic evolution, to NP-complete combinatorial optimization problems. In particular, the subset sum, maximum cut, and mini... | [
163,
1303,
1571,
1980,
2638
] | Train |
2,203 | 2 | Title: CuPit-2: Portable and Efficient High-Level Parallel Programming of Neural Networks for the Systems Analysis Modelling
Abstract: CuPit-2 is a special-purpose programming language designed for expressing dynamic neural network learning algorithms. It provides most of the flexibility of general-purpose languages su... | [
881,
1411,
2397,
2405
] | Train |
2,204 | 1 | Title: University of Nevada Reno Design Strategies for Evolutionary Robotics
Abstract: CuPit-2 is a special-purpose programming language designed for expressing dynamic neural network learning algorithms. It provides most of the flexibility of general-purpose languages such as C or C ++ , but is more expressive. It a... | [
163,
636,
846,
2173
] | Validation |
2,205 | 1 | Title: A Genetic Local Search Approach to the Quadratic Assignment Problem
Abstract: Augmenting genetic algorithms with local search heuristics is a promising approach to the solution of combinatorial optimization problems. In this paper, a genetic local search approach to the quadratic assignment problem (QAP) is pr... | [
1799
] | Validation |
2,206 | 1 | Title: Why Ants are Hard genetic programming, simulated annealing and hill climbing performance is shown not
Abstract: The problem of programming an artificial ant to follow the Santa Fe trail is used as an example program search space. Analysis of shorter solutions shows they have many of the characteristics often asc... | [
1911,
1925,
1984,
2133,
2175,
2261,
2379
] | Train |
2,207 | 4 | Title: Machine Learning Research: Four Current Directions
Abstract: Machine Learning research has been making great progress is many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (a) improving classification accuracy by learning ensemble... | [
79,
1786
] | Train |
2,208 | 3 | Title: Extensions of Fill's algorithm for perfect simulation
Abstract: Fill's algorithm for perfect simulation for attractive finite state space models, unbiased for user impatience, is presented in terms of stochastic recursive sequences and extended in two ways. Repulsive discrete Markov random fields with two codi... | [
126,
1761,
2234,
2235,
2313
] | Train |
2,209 | 4 | Title: PAC Adaptive Control of Linear Systems
Abstract: We consider a special case of reinforcement learning where the environment can be described by a linear system. The states of the environment and the actions the agent can perform are represented by real vectors and the system dynamic is given by a linear equati... | [
2689
] | Train |
2,210 | 6 | Title: An Empirical Analysis of the Benefit of Decision Tree Size Biases as a Function of
Abstract: The results reported here empirically show the benefit of decision tree size biases as a function of concept distribution. First, it is shown how concept distribution complexity (the number of internal nodes in the small... | [
1808
] | Train |
2,211 | 1 | Title: Collective Memory Search 1 Collective Memory Search: Exploiting an Information Center for Exploration
Abstract: The results reported here empirically show the benefit of decision tree size biases as a function of concept distribution. First, it is shown how concept distribution complexity (the number of intern... | [
854,
1232,
1971
] | Validation |
2,212 | 2 | Title: ANALYSIS OF SOUND TEXTURES IN MUSICAL AND MACHINE SOUNDS BY MEANS OF HIGHER ORDER STATISTICAL FEATURES.
Abstract: In this paper we describe a sound classification method, which seems to be applicable to a broad domain of stationary, non-musical sounds, such as machine noises and other man made non periodic sou... | [
2121
] | Validation |
2,213 | 5 | Title: Generating Declarative Language Bias for Top-Down ILP Algorithms
Abstract: Many of today's algorithms for Inductive Logic Programming (ILP) put a heavy burden and responsibility on the user, because their declarative bias have to be defined in a rather low-level fashion. To address this issue, we developed a m... | [
1259,
2126,
2253,
2290,
2539
] | Train |
2,214 | 2 | Title: Behavior Near Zero of the Distribution of GCV Smoothing Parameter Estimates 1
Abstract: Many of today's algorithms for Inductive Logic Programming (ILP) put a heavy burden and responsibility on the user, because their declarative bias have to be defined in a rather low-level fashion. To address this issue, we ... | [
420,
2223
] | Test |
2,215 | 0 | Title: Learning Approximate Control Rules Of High Utility
Abstract: One of the difficult problems in the area of explanation based learning is the utility problem; learning too many rules of low utility can lead to swamping, or degradation of performance. This paper introduces two new techniques for improving the uti... | [
344,
551,
675,
1877,
2057,
2650
] | Train |
2,216 | 1 | Title: Hybridized Crossover-Based Search Techniques for Program Discovery
Abstract: In this paper we address the problem of program discovery as defined by Genetic Programming [10]. We have two major results: First, by combining a hierarchical crossover operator with two traditional single point search algorithms: Si... | [
2361,
2688,
2705
] | Train |
2,217 | 5 | Title: Application of Clausal Discovery to Temporal Databases
Abstract: Most of KDD applications consider databases as static objects, and however many databases are inherently temporal, i.e., they store the evolution of each object with the passage of time. Thus, regularities about the dynamics of these databases ca... | [
1007,
2282
] | Test |
2,218 | 2 | Title: L 0 |The First Four Years Abstract A summary of the progress and plans of
Abstract: Most of KDD applications consider databases as static objects, and however many databases are inherently temporal, i.e., they store the evolution of each object with the passage of time. Thus, regularities about the dynamics of t... | [
2021,
2049,
2337
] | Train |
2,219 | 3 | Title: Exponential Convergence of Langevin Diffusions and Their Discrete Approximations
Abstract: In this paper we consider a continous time method of approximating a given distribution using the Langevin diffusion dL t = dW t + 1 2 r log (L t )dt: We find conditions under which this diffusion converges exponentially... | [
1977,
2008,
2022,
2153
] | Validation |
2,220 | 1 | Title: The Automatic Programming of Agents that Learn Mental Models and Create Simple Plans of Action
Abstract: An essential component of an intelligent agent is the ability to notice, encode, store, and utilize information about its environment. Traditional approaches to program induction have focused on evolving fu... | [
129,
290,
1409,
1940,
1950,
1958,
2139,
2226,
2252,
2478,
2563,
2600
] | Test |
2,221 | 3 | Title: Reasoning about Time and Probability
Abstract: An essential component of an intelligent agent is the ability to notice, encode, store, and utilize information about its environment. Traditional approaches to program induction have focused on evolving functional or reactive programs. This paper presents MAPMAKE... | [
566,
1459,
1527,
1757,
2404
] | Test |
2,222 | 4 | Title: Multi-Time Models for Reinforcement Learning
Abstract: Reinforcement learning can be used not only to predict rewards, but also to predict states, i.e. to learn a model of the world's dynamics. Models can be defined at different levels of temporal abstraction. Multi-time models are models that focus on predict... | [
1954,
2150
] | Test |
2,223 | 2 | Title: Smoothing Spline Models With Correlated Random Errors 1
Abstract: Reinforcement learning can be used not only to predict rewards, but also to predict states, i.e. to learn a model of the world's dynamics. Models can be defined at different levels of temporal abstraction. Multi-time models are models that focus... | [
190,
510,
519,
2214
] | Train |
2,224 | 2 | Title: Design of Optimization Criteria for Multiple Sequence Alignment
Abstract: DIMACS Technical Report 96-53 January 1997 | [
1827
] | Train |
2,225 | 6 | Title: Error-Correcting Output Coding Corrects Bias and Variance
Abstract: Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that learn to classify data points into one of k 2 classes. This p... | [
256,
1608,
1732,
2423
] | Validation |
2,226 | 1 | Title: Simultaneous Evolution of Programs and their Control Structures Simultaneous Evolution of Programs and their Control
Abstract: Previous research has shown that a technique called error-correcting output coding (ECOC) can dramatically improve the classification accuracy of supervised learning algorithms that lear... | [
1950,
1958,
2139,
2220,
2478
] | Validation |
2,227 | 2 | Title: The EM Algorithm for Mixtures of Factor Analyzers
Abstract: Technical Report CRG-TR-96-1 May 21, 1996 (revised Feb 27, 1997) Abstract Factor analysis, a statistical method for modeling the covariance structure of high dimensional data using a small number of latent variables, can be extended by allowing differ... | [
667,
1923,
1974,
2072,
2390
] | Train |
2,228 | 2 | Title: Modeling dynamic receptive field changes in primary visual cortex using inhibitory learning
Abstract: The position, size, and shape of the visual receptive field (RF) of some primary visual cortical neurons change dynamically, in response to artificial scotoma conditioning in cats (Pettet & Gilbert, 1992) and ... | [
127,
1093,
1094,
2068,
2085
] | Train |
2,229 | 5 | Title: Bottom-up induction of logic programs with more than one recursive clause
Abstract: In this paper we present a bottom-up algorithm called MRI to induce logic programs from their examples. This method can induce programs with a base clause and more than one recursive clause from a very small number of examples.... | [
344,
1428,
2663
] | Train |
2,230 | 2 | Title: In Advances in Neural Information Processing Systems 8 Gaussian Processes for Regression
Abstract: The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over... | [
157,
608,
611,
2095
] | Train |
2,231 | 0 | Title: Explaining Anomalies as a Basis for Knowledge Base Refinement
Abstract: Explanations play a key role in operationalization-based anomaly detection techniques. In this paper we show that their role is not limited to anomaly detection; they can also be used for guiding automated knowledge base refinement. We int... | [
136,
2635
] | Train |
2,232 | 1 | Title: Facing The Facts: Necessary Requirements For The Artificial Evolution of Complex Behaviour
Abstract: This paper sets out a conceptual framework for the open-ended artificial evolution of complex behaviour in autonomous agents. If recurrent dynamical neural networks (or similar) are used as phenotypes, then a G... | [
163,
411,
2058
] | Validation |
2,233 | 2 | Title: An unsupervised neural network for low-level control of a wheeled mobile robot: noise resistance, stability,
Abstract: We have recently introduced a neural network mobile robot controller (NETMORC) that autonomously learns the forward and inverse odometry of a differential drive robot through an unsupervised lea... | [
636,
703,
2201
] | Validation |
2,234 | 3 | Title: Perfect Sampling of Harris Recurrent Markov Chains
Abstract: We develop an algorithm for simulating "perfect" random samples from the invariant measure of a Harris recurrent Markov chain. The method uses backward coupling of embedded regeneration times, and works most effectively for finite chains and for stoc... | [
2208
] | Train |
2,235 | 3 | Title: EXACT SIMULATION USING MARKOV CHAINS
Abstract: This reports gives a review of the new exact simulation algorithms using Markov chains. The first part covers the discrete case. We consider two different algorithms, Propp and Wilsons coupling from the past (CFTP) technique and Fills rejection sampler. The algori... | [
2208
] | Train |
2,236 | 2 | Title: Robust Convergence of Two-Stage Nonlinear Algorithms for Identification in H 1
Abstract: | [
2262,
2435,
2542
] | Train |
2,237 | 1 | Title: Specialization under Social Conditions in Shared Environments
Abstract: Specialist and generalist behaviors in populations of artificial neural networks are studied. A genetic algorithm is used to simulate evolution processes, and thereby to develop neural network control systems that exhibit specialist or gen... | [
2170,
2274
] | Test |
2,238 | 1 | Title: Where Does the Good Stuff Go, and Why? How contextual semantics influences program structure in
Abstract: Using deliberately designed primitive sets, we investigate the relationship between context-based expression mechanisms and the size, height and density of genetic program trees during the evolutionary proce... | [
2271
] | Test |
2,239 | 2 | Title: Predicting Conditional Probability Distributions: A Connectionist Approach
Abstract: Most traditional prediction techniques deliver the mean of the probability distribution (a single point). For multimodal processes, instead of predicting the mean of the probability distribution, it is important to predict the... | [
587,
1366,
2413,
2414,
2507,
2513
] | Train |
2,240 | 5 | Title: Stable ILP Exploring the Added Expressivity of Negation in the Background Knowledge
Abstract: We present stable ILP, a cross-disciplinary concept straddling machine learning and nonmonotonic reasoning. Stable models give meaning to logic programs containing negative assertions. In stable ILP, we employ stable ... | [
2466
] | Validation |
2,241 | 3 | Title: On Decision-Theoretic Foundations for Defaults
Abstract: In recent years, considerable effort has gone into understanding default reasoning. Most of this effort concentrated on the question of entailment, i.e., what conclusions are warranted by a knowledge-base of defaults. Surprisingly, few works formally exa... | [
1994
] | Train |
2,242 | 3 | Title: Density estimation by wavelet thresholding
Abstract: Density estimation is a commonly used test case for non-parametric estimation methods. We explore the asymptotic properties of estimators based on thresholding of empirical wavelet coefficients. Minimax rates of convergence are studied over a large range of ... | [
1910,
2458,
2661
] | Train |
2,243 | 2 | Title: Using Precepts to Augment Training Set Learning an input whose value is don't-care in some
Abstract: are used in turn to approximate A. Empirical studies show that good results can be achieved with TSL [8, 11]. However, TSL has several drawbacks. Training set learners (e.g., backpropagation) are typically slow a... | [
831,
2244,
2245
] | Train |
2,244 | 0 | Title: AN INCREMENTAL LEARNING MODEL FOR COMMONSENSE REASONING
Abstract: | [
2243
] | Train |
2,245 | 0 | Title: AN EFFICIENT METRIC FOR HETEROGENEOUS INDUCTIVE LEARNING APPLICATIONS IN THE ATTRIBUTE-VALUE LANGUAGE 1
Abstract: Many inductive learning problems can be expressed in the classical attribute-value language. In order to learn and to generalize, learning systems often rely on some measure of similarity between t... | [
87,
2243,
2471
] | Train |
2,246 | 6 | Title: Learning to model sequences generated by switching distributions
Abstract: We study efficient algorithms for solving the following problem, which we call the switching distributions learning problem. A sequence S = 1 2 : : : n , over a finite alphabet S is generated in the following way. The sequence is a conc... | [
2356,
2475
] | Train |
2,247 | 2 | Title: A Connectionist Architecture with Inherent Systematicity
Abstract: For connectionist networks to be adequate for higher level cognitive activities such as natural language interpretation, they have to generalize in a way that is appropriate given the regularities of the domain. Fodor and Pylyshyn (1988) identi... | [
2263,
2701
] | Train |
2,248 | 1 | Title: Heuristic for Improved Genetic Bin Packing
Abstract: University of Tulsa Technical Report UTULSA-MCS-93-8, May, 1993. Submitted to Information Processing Letters, May, 1993. | [
145,
163,
2296
] | Train |
2,249 | 1 | Title: Using a Distance Metric on Genetic Programs to Understand Genetic Operators
Abstract: I describe a distance metric called "edit" distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount... | [
2175,
2271
] | Train |
2,250 | 1 | Title: The Impact of External Dependency in Genetic Programming Primitives
Abstract: Both control and data dependencies among primitives impact the behavioural consistency of subprograms in genetic programming solutions. Behavioural consistency in turn impacts the ability of genetic programming to identify and promot... | [
1696,
1940,
2175,
2271
] | Train |
2,251 | 1 | Title: A PARALLEL ISLAND MODEL GENETIC ALGORITHM FOR THE MULTIPROCESSOR SCHEDULING PROBLEM
Abstract: In this paper we compare the performance of a serial and a parallel island model Genetic Algorithm for solving the Multiprocessor Scheduling Problem. We show results using fixed and scaled problems both using and not ... | [
145,
163,
2296
] | Test |
2,252 | 1 | Title: Neural Programming and an Internal Reinforcement Policy
Abstract: An important reason for the continued popularity of Artificial Neural Networks (ANNs) in the machine learning community is that the gradient-descent backpropagation procedure gives ANNs a locally optimal change procedure and, in addition, a fram... | [
2220,
2271,
2277
] | Train |
2,253 | 5 | Title: Top-down Induction of Logical Decision Trees
Abstract: A first order framework for top-down induction of logical decision trees is introduced. Logical decision trees are more expressive than the flat logic programs typically induced by empirical inductive logic programming systems because logical decision tree... | [
2213,
2591
] | Train |
2,254 | 1 | Title: An Indexed Bibliography of Genetic Algorithms: Years 1957-1993 compiled by
Abstract: A first order framework for top-down induction of logical decision trees is introduced. Logical decision trees are more expressive than the flat logic programs typically induced by empirical inductive logic programming systems... | [
2039,
2347
] | Train |
2,255 | 1 | Title: Evolutionary Learning of the Crossover Operator
Abstract: 1 Abstract | [
427,
2412
] | Train |
2,256 | 2 | Title: Improved Center Point Selection for Probabilistic Neural Networks
Abstract: Probabilistic Neural Networks (PNN) typically learn more quickly than many neural network models and have had success on a variety of applications. However, in their basic form, they tend to have a large number of hidden nodes. One com... | [
2597
] | Train |
2,257 | 1 | Title: Real-time Interactive Neuro-evolution
Abstract: In standard neuro-evolution, a population of networks is evolved in the task, and the network that best solves the task is found. This network is then fixed and used to solve future instances of the problem. Networks evolved in this way do not handle real-time in... | [
22,
247,
1767,
1768,
2444
] | Test |
2,258 | 2 | Title: LU TP 93-24 Predicting System Loads with Artificial Neural
Abstract: Networks Methods and Results from Abstract: We devise a feed-forward Artificial Neural Network (ANN) procedure for predicting utility loads and present the resulting predictions for two test problems given by "The Great Energy Predictor Shoot... | [
427,
1887
] | Train |
2,259 | 1 | Title: An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming
Abstract: In this paper we first review the main results in the theory of schemata in Genetic Programming (GP) and summarise a new GP schema theory which is based on a new definition of schema. Then we study the crea... | [
163,
1257,
1959,
2175,
2261,
2271
] | Train |
2,260 | 2 | Title: Radial Basis Functions for Process Control
Abstract: Radial basis function (RBFs) neural networks provide an attractive method for high dimensional nonparametric estimation for use in nonlinear control. They are faster to train than conventional feedforward networks with sigmoidal activation networks ("backpro... | [
611,
2501
] | Test |
2,261 | 1 | Title: Genetic Programming with One-Point Crossover and Point Mutation
Abstract: Technical Report: CSRP-97-13 April 1997 Abstract In recent theoretical and experimental work on schemata in genetic programming we have proposed a new simpler form of crossover in which the same crossover point is selected in both parent... | [
1719,
2087,
2206,
2259
] | Test |
2,262 | 2 | Title: OPTIMAL ASYMPTOTIC IDENTIFICATION UNDER BOUNDED DISTURBANCES
Abstract: This paper investigates the intrinsic limitation of worst-case identification of LTI systems using data corrupted by bounded disturbances, when the unknown plant is known to belong to a given model set. This is done by analyzing the optimal... | [
2236,
2435,
2542
] | Train |
2,263 | 2 | Title: A Connectionist Architecture for Learning to Parse
Abstract: We present a connectionist architecture and demonstrate that it can learn syntactic parsing from a corpus of parsed text. The architecture can represent syntactic constituents, and can learn generalizations over syntactic constituents, thereby addres... | [
2247,
2701
] | Train |
2,264 | 1 | Title: Evolutionary Computation in Air Traffic Control Planning
Abstract: Air Traffic Control is involved in the real-time planning of aircraft trajectories. This is a heavily constrained optimization problem. We concentrate on free-route planning, in which aircraft are not required to fly over way points. The choice... | [
2519
] | Train |
2,265 | 1 | Title: AN APPROACH TO A PROBLEM IN NETWORK DESIGN USING GENETIC ALGORITHMS
Abstract: Air Traffic Control is involved in the real-time planning of aircraft trajectories. This is a heavily constrained optimization problem. We concentrate on free-route planning, in which aircraft are not required to fly over way points.... | [
163,
2347
] | Train |
2,266 | 3 | Title: A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian
Abstract: We describe the maximum-likelihood parameter estimation problem and how the Expectation-Maximization (EM) algorithm can be used for its solution. We first describe the abstract form of the EM algorithm as it ... | [
74,
345,
2421
] | Validation |
2,267 | 1 | Title: Evolving Optimal Neural Networks Using Genetic Algorithms with Occam's Razor
Abstract: Genetic algorithms have been used for neural networks in two main ways: to optimize the network architecture and to train the weights of a fixed architecture. While most previous work focuses on only one of these two options... | [
2196
] | Test |
2,268 | 2 | Title: SPERT: A VLIW/SIMD Microprocessor for Artificial Neural Network Computations
Abstract: SPERT (Synthetic PERceptron Testbed) is a fully programmable single chip microprocessor designed for efficient execution of artificial neural network algorithms. The first implementation will be in a 1.2 m CMOS technology wi... | [
1753,
2275,
2445
] | Train |
2,269 | 1 | Title: Some Steps Towards a Form of Parallel Distributed Genetic Programming
Abstract: Genetic Programming is a method of program discovery consisting of a special kind of genetic algorithm capable of operating on nonlinear chromosomes (parse trees) representing programs and an interpreter which can run the programs ... | [
2277
] | Train |
2,270 | 2 | Title: Using generative models for handwritten digit recognition
Abstract: Genetic Programming is a method of program discovery consisting of a special kind of genetic algorithm capable of operating on nonlinear chromosomes (parse trees) representing programs and an interpreter which can run the programs being optimi... | [
480,
667,
2043
] | Train |
2,271 | 1 | Title: How Fitness Structure Affects Subsolution Acquisition in Genetic Programming
Abstract: We define fitness structure in genetic programming to be the mapping between the subprograms of a program and their respective fitness values. This paper shows how various fitness structures of a problem with independent sub... | [
2238,
2249,
2250,
2252,
2259
] | Validation |
2,272 | 2 | Title: Rapid learning of binding-match and binding-error detector circuits via long-term potentiation
Abstract: It is argued that the memorization of events and situations (episodic memory) requires the rapid formation of neural circuits responsive to binding errors and binding matches. While the formation of circuit... | [
1176,
1866
] | Train |
2,273 | 6 | Title: Learning Harmonic Progression Using Markov Models EECS545 Project
Abstract: It is argued that the memorization of events and situations (episodic memory) requires the rapid formation of neural circuits responsive to binding errors and binding matches. While the formation of circuits responsive to binding match... | [
2360
] | Train |
2,274 | 1 | Title: Specialization in Populations of Artificial Neural Networks
Abstract: Specialization in populations of artificial neural networks is studied. Organisms with both fixed and evolvable fitness formulae are placed in isolated and shared environments, and the emerged behaviors are compared. An evolvable fitness for... | [
163,
2237
] | Test |
2,275 | 2 | Title: Connectionist Layered Object-Oriented Network Simulator (CLONES): User's Manual minimize the learning curve for using CLONES,
Abstract: CLONES is a object-oriented library for constructing, training and utilizing layered connectionist networks. The CLONES library contains all the object classes needed to write a... | [
1120,
2191,
2268,
2445,
2522
] | Train |
2,276 | 0 | Title: Finding Analogues for Innovative Design
Abstract: Knowledge Systems Laboratory March 1995 Report No. KSL 95-32 | [
30,
486,
1864
] | Test |
2,277 | 1 | Title: Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming
Abstract: Technical Report: CSRP-96-14 August 1996 Abstract Genetic Programming is a method of program discovery consisting of a special kind of genetic algorithm capable of operating on parse trees represen... | [
1277,
1931,
2252,
2269,
2624
] | Train |
2,278 | 2 | Title: Routing in Optical Multistage Interconnection Networks: a Neural Network Solution
Abstract: There has been much interest in using optics to implement computer interconnection networks. However, there has been little discussion of any routing methodologies besides those already used in electronics. In this pape... | [
2284
] | Train |
2,279 | 2 | Title: Quicknet on MultiSpert: Fast Parallel Neural Network Training
Abstract: The MultiSpert parallel system is a straight-forward extension of the Spert workstation accelerator, which is predominantly used in speech recognition research at ICSI. In order to deliver high performance for Artificial Neural Network tra... | [
1806,
2579
] | Train |
2,280 | 1 | Title: A GENETIC ALGORITHM FOR FRAGMENT ALLOCATION IN A DISTRIBUTED DATABASE SYSTEM
Abstract: In this paper we explore the distributed database allocation problem, which is intractable. We also discuss genetic algorithms and how they have been used successfully to solve combinatorial problems. Our experimental result... | [
145,
163,
2286
] | Test |
2,281 | 2 | Title: GENE REGULATION AND BIOLOGICAL DEVELOPMENT IN NEURAL NETWORKS: AN EXPLORATORY MODEL
Abstract: In this paper we explore the distributed database allocation problem, which is intractable. We also discuss genetic algorithms and how they have been used successfully to solve combinatorial problems. Our experimental... | [
1134,
2429
] | Train |
2,282 | 5 | Title: The ILP description learning problem: Towards a general model-level definition of data mining in ILP
Abstract: stefan.wrobel@gmd.de, saso.dzeroski@gmd.de Proc. FGML-95, Annual Workshop of the GI Special Interest Group Machine Learning (GI FG 1.1.3), ed. K. Morik and J. Herrmann, Research Report 580, Univ.Dortm... | [
1686,
2217,
2426
] | Test |
2,283 | 2 | Title: Predictive Control of Opto-Electronic Reconfigurable Interconnection Networks Using Neural Networks
Abstract: Opto-electronic reconfigurable interconnection networks are limited by significant control latency when used in large multiprocessor systems. This latency is the time required to analyze the current tr... | [
2284
] | Validation |
2,284 | 2 | Title: Performance of On-Line Learning Methods in Predicting Multiprocessor Memory Access Patterns
Abstract: Technical Report UMIACS-TR-96-59 and CS-TR-3676 Institute for Advanced Computer Studies University of Maryland College Park, MD 20742 Abstract Shared memory multiprocessors require reconfigurable interconnecti... | [
74,
1293,
2278,
2283
] | Validation |
2,285 | 3 | Title: Simulation Based Bayesian Nonparametric Regression Methods
Abstract: Technical Report UMIACS-TR-96-59 and CS-TR-3676 Institute for Advanced Computer Studies University of Maryland College Park, MD 20742 Abstract Shared memory multiprocessors require reconfigurable interconnection networks (INs) for scalability... | [
2138,
2311
] | Train |
2,286 | 1 | Title: A Genetic Algorithm for File and Task Placement in a Distributed System
Abstract: In this paper we explore the distributed file and task placement problem, which is intractable. We also discuss genetic algorithms and how they have been used successfully to solve combinatorial problems. Our experimental results... | [
145,
163,
2280
] | Train |
2,287 | 3 | Title: Consistency of Posterior Distributions for Neural Networks
Abstract: In this paper we show that the posterior distribution for feedforward neural networks is asymptotically consistent. This paper extends earlier results on universal approximation properties of neural networks to the Bayesian setting. The proof... | [
560,
2315
] | Validation |
2,288 | 3 | Title: Anytime Influence Diagrams
Abstract: In this paper we show that the posterior distribution for feedforward neural networks is asymptotically consistent. This paper extends earlier results on universal approximation properties of neural networks to the Bayesian setting. The proof of consistency embeds the probl... | [
1759,
2697
] | Validation |
2,289 | 0 | Title: An Interactive Planning Architecture The Forest Fire Fighting case
Abstract: This paper describes an interactive planning system that was developed inside an Intelligent Decision Support System aimed at supporting an operator when planning the initial attack to forest fires. The planning architecture rests on ... | [
1804,
1805
] | Train |
2,290 | 5 | Title: A Comparison of Pruning Methods for Relational Concept Learning
Abstract: Pre-Pruning and Post-Pruning are two standard methods of dealing with noise in concept learning. Pre-Pruning methods are very efficient, while Post-Pruning methods typically are more accurate, but much slower, because they have to genera... | [
344,
378,
585,
1275,
2213,
2291
] | Train |
2,291 | 5 | Title: Top-Down Pruning in Relational Learning
Abstract: Pruning is an effective method for dealing with noise in Machine Learning. Recently pruning algorithms, in particular Reduced Error Pruning, have also attracted interest in the field of Inductive Logic Programming. However, it has been shown that these methods ... | [
344,
378,
585,
1275,
2290
] | Train |
2,292 | 3 | Title: Logarithmic-Time Updates and Queries in Probabilistic Networks
Abstract: Traditional databases commonly support efficient query and update procedures that operate in time which is sublinear in the size of the database. Our goal in this paper is to take a first step toward dynamic reasoning in probabilistic dat... | [
1111,
1899,
2140
] | Test |
2,293 | 3 | Title: Localized Partial Evaluation of Belief Networks
Abstract: in the network. Often, however, an application will not need information about every node in the network nor will it need exact probabilities. We present the localized partial evaluation (LPE) propagation algorithm, which computes interval bounds on the... | [
1937
] | Validation |
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