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1-hop neighbor's text information: Exploratory Learning in the Game of GO: Initial Results. : This paper considers the importance of exploration to game-playing programs which learn by playing against opponents. The central question is whether a learning program should play the move which offers the best chance of win... | 5 | Reinforcement Learning | cora | 1,701 | test |
1-hop neighbor's text information: Generalization in reinforcement learning: Safely approximating the value function. : To appear in: G. Tesauro, D. S. Touretzky and T. K. Leen, eds., Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995. A straightforward approach to the curse of dimensio... | 5 | Reinforcement Learning | cora | 170 | test |
1-hop neighbor's text information: "Mass Reconstruction with a Neural Network", : A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W ! q q, where W -bosons are produced in pp reactions at SPS collider ene... | 1 | Neural Networks | cora | 2,576 | test |
1-hop neighbor's text information: "The third generation of neural network models," : The computational power of formal models for networks of spiking neurons is compared with that of other neural network models based on McCulloch Pitts neurons (i.e. threshold gates) respectively sigmoidal gates. In particular it is s... | 1 | Neural Networks | cora | 43 | test |
1-hop neighbor's text information: Co-evolving soccer softbot team coordination with genetic programming. In RoboCup-97: The first robot world cup soccer games and conferences. : Genetic Programming is a promising new method for automatically generating functions and algorithms through natural selection. In contrast t... | 5 | Reinforcement Learning | cora | 87 | test |
1-hop neighbor's text information: ON MCMC METHODS IN BAYESIAN REGRESSION ANALYSIS AND MODEL SELECTION: The objective of statistical data analysis is not only to describe the behaviour of a system, but also to propose, construct (and then to check) a model of observed processes. Bayesian methodology offers one of possi... | 6 | Probabilistic Methods | cora | 1,338 | test |
1-hop neighbor's text information: An investigation of marker-passing algorithms for analogue retrieval. : If analogy and case-based reasoning systems are to scale up to very large case bases, it is important to analyze the various methods used for retrieving analogues to identify the features of the problem for which... | 2 | Case Based | cora | 410 | test |
1-hop neighbor's text information: Query by Committee, : We propose an algorithm called query by committee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and p... | 4 | Theory | cora | 1,208 | test |
1-hop neighbor's text information: A theory of inferred causation. : This paper concerns the empirical basis of causation, and addresses the following issues: We propose a minimal-model semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious cova... | 6 | Probabilistic Methods | cora | 1,942 | test |
1-hop neighbor's text information: A machine learning library in C++. : We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety ... | 6 | Probabilistic Methods | cora | 1,641 | test |
1-hop neighbor's text information: `Machine learning in prognosis of the femoral neck fracture recovery\', : We compare the performance of several machine learning algorithms in the problem of prognos-tics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, ba... | 1 | Neural Networks | cora | 1,491 | val |
1-hop neighbor's text information: The EM algorithm for mixtures of factor analyzers. : 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 extende... | 1 | Neural Networks | cora | 932 | val |
1-hop neighbor's text information: Multiagent reinforcement learning: Theoretical framework and an algorithm. : In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games to a broader framework. We de... | 5 | Reinforcement Learning | cora | 1,418 | train |
1-hop neighbor's text information: Using qualitative relationships for bounding probability distributions. : We exploit qualitative probabilistic relationships among variables for computing bounds of conditional probability distributions of interest in Bayesian networks. Using the signs of qualitative relationships, w... | 6 | Probabilistic Methods | cora | 1,124 | test |
1-hop neighbor's text information: A theory of inferred causation. : This paper concerns the empirical basis of causation, and addresses the following issues: We propose a minimal-model semantics of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious cova... | 6 | Probabilistic Methods | cora | 1,565 | test |
1-hop neighbor's text information: Applying machine learning to agricultural data. : Many techniques have been developed for learning rules and relationships automatically from diverse data sets, to simplify the often tedious and error-prone process of acquiring knowledge from empirical data. While these techniques ar... | 0 | Rule Learning | cora | 1,427 | test |
1-hop neighbor's text information: Coordinating Reactive Behaviors keywords: reactive systems, planning and learning: Combinating reactivity with planning has been proposed as a means of compensating for potentially slow response times of planners while still making progress toward long term goals. The demands of rapi... | 1 | Neural Networks | cora | 1,025 | test |
1-hop neighbor's text information: PAC-learning recursive logic programs: Efficient algorithms. : We present algorithms that learn certain classes of function-free recursive logic programs in polynomial time from equivalence queries. In particular, we show that a single k-ary recursive constant-depth determinate claus... | 0 | Rule Learning | cora | 322 | train |
1-hop neighbor's text information: Incremental induction of decision trees. : Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of decis... | 2 | Case Based | cora | 1,602 | train |
1-hop neighbor's text information: On Computing the Largest Fraction of Missing Information for the EM Algorithm and the Worst: We address the problem of computing the largest fraction of missing information for the EM algorithm and the worst linear function for data augmentation. These are the largest eigenvalue and i... | 6 | Probabilistic Methods | cora | 1,161 | val |
1-hop neighbor's text information: A defect in Dempster-Shafer theory. : By analyzing the relationships among chance, weight of evidence and degree of belief, it is shown that the assertion "chances are special cases of belief functions" and the assertion "Dempster's rule can be used to combine belief functions based ... | 6 | Probabilistic Methods | cora | 1,064 | test |
1-hop neighbor's text information: Integrated Architectures for Learning, Planning and Reacting Based on Approximating Dynamic Programming, : This paper extends previous work with Dyna, a class of architectures for intelligent systems based on approximating dynamic programming methods. Dyna architectures integrate tri... | 5 | Reinforcement Learning | cora | 149 | test |
1-hop neighbor's text information: Learning Concept Classification Rules Using Genetic Algorithms. : In this paper, we explore the use of genetic algorithms (GAs) as a key element in the design and implementation of robust concept learning systems. We describe and evaluate a GA-based system called GABIL that continual... | 4 | Theory | cora | 1,938 | test |
1-hop neighbor's text information: Combining Inductive Learning with Prior Knowledge and Reasoning. : Much effort has been devoted to understanding learning and reasoning in artificial intelligence. However, very few models attempt to integrate these two complementary processes. Rather, there is a vast body of researc... | 2 | Case Based | cora | 1,894 | val |
1-hop neighbor's text information: DISTRIBUTED GENETIC ALGORITHMS FOR PARTITIONING UNIFORM GRIDS:
1-hop neighbor's text information: Genetic Algorithms as Multi-Coordinators in Large-Scale Optimization: We present high-level, decomposition-based algorithms for large-scale block-angular optimization problems containing... | 3 | Genetic Algorithms | cora | 1,776 | train |
1-hop neighbor's text information: (1997a) Bayesian time series: Models and computations for the analysis of time series in the physical sciences. In Maximum Entropy and Bayesian Methods 15, : This articles discusses developments in Bayesian time series mod-elling and analysis relevant in studies of time series in the... | 6 | Probabilistic Methods | cora | 2,008 | test |
1-hop neighbor's text information: Graphical Models in Applied Multivariate Statistics. :
1-hop neighbor's text information: Operations for learning with graphical models. : This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphica... | 6 | Probabilistic Methods | cora | 134 | test |
1-hop neighbor's text information: Genetic Algorithms in Search, Optimization and Machine Learning. : Angeline, P., Saunders, G. and Pollack, J. (1993) An evolutionary algorithm that constructs recurrent neural networks, LAIR Technical Report #93-PA-GNARLY, Submitted to IEEE Transactions on Neural Networks Special Iss... | 3 | Genetic Algorithms | cora | 532 | test |
1-hop neighbor's text information: Supervised learning from incomplete data via an EM approach. : Real-world learning tasks may involve high-dimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data set... | 6 | Probabilistic Methods | cora | 45 | val |
1-hop neighbor's text information: Supervised and unsupervised discretization of continuous features. : Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify defining characteristics of the methods, and conduc... | 0 | Rule Learning | cora | 817 | test |
1-hop neighbor's text information: Static Data Association with a Terrain-Based Prior Density:
Target text information: Selection of Distance Metrics and Feature Subsets for k-Nearest Neighbor Classifiers. :
I provide the content of the target node and its neighbors' information. The relation between the target node... | 1 | Neural Networks | cora | 2,180 | test |
1-hop neighbor's text information: Selection of Relevant Features in Machine Learning. : In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of se... | 1 | Neural Networks | cora | 233 | val |
1-hop neighbor's text information: MML and Bayesianism: similarities and differences. : Tech Report 207 Department of Computer Science, Monash University, Clayton, Vic. 3168, Australia Abstract: This paper continues the introduction to minimum encoding inductive inference given by Oliver and Hand. This series of paper... | 6 | Probabilistic Methods | cora | 432 | test |
1-hop neighbor's text information: Rigorous learning curve bounds from statistical mechanics. : In this paper we introduce and investigate a mathematically rigorous theory of learning curves that is based on ideas from statistical mechanics. The advantage of our theory over the well-established Vapnik-Chervonenkis the... | 4 | Theory | cora | 2,695 | test |
1-hop neighbor's text information: Identification and control of nonlinear systems using neural network models: Design and stability analysis. : Report 91-09-01 September 1991 (revised) May 1994
Target text information: Some topics in neural networks and control. :
I provide the content of the target node and its ne... | 1 | Neural Networks | cora | 1,560 | test |
1-hop neighbor's text information: Strongly typed genetic programming in evolving cooperation strategies. :
1-hop neighbor's text information: Type inheritance in strongly typed genetic programming. : This paper appears as chapter 18 of Kenneth E. Kinnear, Jr. and Peter J. Angeline, editors Advances in Genetic Progr... | 3 | Genetic Algorithms | cora | 2,064 | test |
1-hop neighbor's text information: Olfaction Metal Oxide Semiconductor Gas Sensors and Neural Networks:
Target text information: ``Gas Identification System using Graded Temperature Sensor and Neural Net Interpretation\'\', :
I provide the content of the target node and its neighbors' information. The relation betwe... | 1 | Neural Networks | cora | 1,976 | test |
1-hop neighbor's text information: "Using Modeling Knowledge to Guide Design Space Search". : Automated search of a space of candidate designs seems an attractive way to improve the traditional engineering design process. To make this approach work, however, the automated design system must include both knowledge of t... | 3 | Genetic Algorithms | cora | 2,077 | test |
1-hop neighbor's text information: Modelling risk from a disease in time and space, : This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time- and space-varying covariate effects. Data analysis is implemented via Markov ch... | 6 | Probabilistic Methods | cora | 2,251 | test |
1-hop neighbor's text information: Regression shrinkage and selection via the lasso. : We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constr... | 6 | Probabilistic Methods | cora | 407 | val |
1-hop neighbor's text information: Neuro-dynamic Programming. :
1-hop neighbor's text information: Correlated action effects in decision-theoretic regression. : Much recent research in decision theoretic planning has adopted Markov decision processes (MDPs) as the model of choice, and has attempted to make their sol... | 6 | Probabilistic Methods | cora | 221 | train |
1-hop neighbor's text information: "Automated WYSIWYG Design of both the topology and component val 223 ues of electrical circuits using genetic programming," : Genetic programming was used to evolve both the topology and sizing (numerical values) for each component of a low-distortion, low
1-hop neighbor's text infor... | 3 | Genetic Algorithms | cora | 2,110 | test |
1-hop neighbor's text information: Monitoring in Embedded Agents: Finding good monitoring strategies is an important process in the design of any embedded agent. We describe the nature of the monitoring problem, point out what makes it difficult, and show that while periodic monitoring strategies are often the easiest ... | 3 | Genetic Algorithms | cora | 1,083 | test |
1-hop neighbor's text information: Markov chain Monte Carlo methods based on "slicing" the density function. : 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 Mark... | 6 | Probabilistic Methods | cora | 958 | test |
1-hop neighbor's text information: `A case study in machine learning\', : 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 generating decision trees and prediction rules from cases ... | 0 | Rule Learning | cora | 2,344 | test |
1-hop neighbor's text information: Global conditioning for probabilistic inference in belief networks. : In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loop-cutset conditioning. We show that globa... | 6 | Probabilistic Methods | cora | 693 | test |
1-hop neighbor's text information: Slonim. The power of team exploration: Two robots can learn unlabeled directed graphs. : We show that two cooperating robots can learn exactly any strongly-connected directed graph with n indistinguishable nodes in expected time polynomial in n. We introduce a new type of homing sequ... | 4 | Theory | cora | 929 | test |
1-hop neighbor's text information: Evolving sensors in environments of controlled complexity. : 1 . Sensors represent a crucial link between the evolutionary forces shaping a species' relationship with its environment, and the individual's cognitive abilities to behave and learn. We report on experiments using a new c... | 3 | Genetic Algorithms | cora | 173 | test |
1-hop neighbor's text information: On the usefulness of re-using diagnostic solutions. : Recent studies on planning, comparing plan re-use and plan generation, have shown that both the above tasks may have the same degree of computational complexity, even if we deal with very similar problems. The aim of this paper is... | 2 | Case Based | cora | 178 | test |
1-hop neighbor's text information: The Neural Network House: An overview. : Typical home comfort systems utilize only rudimentary forms of energy management and conservation. The most sophisticated technology in common use today is an automatic setback thermostat. Tremendous potential remains for improving the efficie... | 1 | Neural Networks | cora | 1,789 | test |
1-hop neighbor's text information: Dirichlet mixtures: A method for improving detection of weak but significant protein sequence homology. COS. : This paper presents the mathematical foundations of Dirichlet mixtures, which have been used to improve database search results for homologous sequences, when a variable num... | 1 | Neural Networks | cora | 554 | val |
1-hop neighbor's text information: Smoothing spline ANOVA with component-wise Bayesian "confidence intervals". : We study a multivariate smoothing spline estimate of a function of several variables, based on an ANOVA decomposition as sums of main effect functions (of one variable), two-factor interaction functions (of... | 1 | Neural Networks | cora | 767 | test |
1-hop neighbor's text information: Vytopil. Design Issues Towards PREENS, a Parallel Research Execution Environment for Neural Systems. : PREENS a Parallel Research Execution Environment for Neural Systems is a distributed neurosimulator, targeted on networks of workstations and transputer systems. As current applicat... | 1 | Neural Networks | cora | 211 | test |
1-hop neighbor's text information: Kanazawa, Reasoning about Time and Probability, :
1-hop neighbor's text information: Fall diagnosis using dynamic belief networks. : The task is to monitor walking patterns and give early warning of falls using foot switch and mercury trigger sensors. We describe a dynamic belief n... | 6 | Probabilistic Methods | cora | 2,083 | test |
1-hop neighbor's text information: A collection of algorithms for belief networks. : Portions of this report have been published in the Proceedings of the Fifteenth Annual Symposium on Computer Applications in Medical Care (November, 1991).
Target text information: : Figure 9: Results for various optimizations. Figure... | 6 | Probabilistic Methods | cora | 869 | val |
1-hop neighbor's text information: A Theory of Networks for Approximation and Learning, : Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multi-dimensional function, that is solving th... | 1 | Neural Networks | cora | 979 | test |
1-hop neighbor's text information: Boolean Functions Fitness Spaces: We investigate the distribution of performance of the Boolean functions of 3 Boolean inputs (particularly that of the parity functions), the always-on-6 and even-6 parity functions. We us enumeration, uniform Monte-Carlo random sampling and sampling r... | 3 | Genetic Algorithms | cora | 2,100 | test |
1-hop neighbor's text information: Abduction, experience, and goals: A model of everyday abductive explanation. :
1-hop neighbor's text information: Adapter: an integrated diagnostic system combining case-based and abductive reasoning. : The aim of this paper is to describe the ADAPtER system, a diagnostic architect... | 2 | Case Based | cora | 2,616 | test |
1-hop neighbor's text information: Classifiers: A theoretical and empirical study. : This paper describes how a competitive tree learning algorithm can be derived from first principles. The algorithm approximates the Bayesian decision theoretic solution to the learning task. Comparative experiments with the algorithm ... | 4 | Theory | cora | 152 | train |
1-hop neighbor's text information: Exploratory Modelling of Multiple Non-stationary Time Series: Latent Process Structure & Decompositions," in Modelling Longitudinal and Spatially Correlated Data, : We describe and illustrate Bayesian approaches to modelling and analysis of multiple non-stationary time series. This b... | 6 | Probabilistic Methods | cora | 2,004 | test |
1-hop neighbor's text information: Experiments with a New Boosting Algorithm. : In an earlier paper, we introduced a new boosting algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little... | 4 | Theory | cora | 456 | test |
1-hop neighbor's text information: An incremental interactive algorithm for regular grammar inference. : We present an efficient incremental algorithm for learning deterministic finite state automata (DFA) from labeled examples and membership queries. This algorithm is an extension of Angluin's ID procedure to an incr... | 4 | Theory | cora | 1,093 | test |
1-hop neighbor's text information: Paying attention to the right things: Issues of focus in case-based creative design. : Case-based reasoning can be used to explain many creative design processes, since much creativity stems from using old solutions in novel ways. To understand the role cases play, we conducted an ex... | 2 | Case Based | cora | 1,750 | test |
1-hop neighbor's text information: A probabilistic calculus of actions. : We present a symbolic machinery that admits both probabilistic and causal information about a given domain, and produces probabilistic statements about the effect of actions and the impact of observations. The calculus admits two types of condit... | 6 | Probabilistic Methods | cora | 874 | val |
1-hop neighbor's text information: On the convergence properties of the EM algorithm. : In this article we investigate the relationship between the two popular algorithms, the EM algorithm and the Gibbs sampler. We show that the approximate rate of convergence of the Gibbs sampler by Gaussian approximation is equal to... | 6 | Probabilistic Methods | cora | 2,295 | val |
1-hop neighbor's text information: Combining neural and symbolic learning to revise probabilistic rule bases. : This paper describes Rapture | a system for revising probabilistic knowledge bases that combines connectionist and symbolic learning methods. Rapture uses a modified version of backpropagation to refine the ... | 1 | Neural Networks | cora | 257 | test |
1-hop neighbor's text information: Simple Synchrony Networks: Learning Generalisations across Syntactic Constituents: This paper describes a training algorithm for Simple Synchrony Networks (SSNs), and reports on experiments in language learning using a recursive grammar. The SSN is a new connectionist architecture com... | 1 | Neural Networks | cora | 1,831 | test |
1-hop neighbor's text information: Generalizations of the bias/variance decomposition for prediction error. : The bias and variance of a real valued random variable, using squared error loss, are well understood. However because of recent developments in classification techniques it has become desirable to extend thes... | 4 | Theory | cora | 1,462 | test |
1-hop neighbor's text information: "Coevolving High Level Representations," :
1-hop neighbor's text information: The evolution of communication schemes over continuous channaels. : Many problems impede the design of multi-agent systems, not the least of which is the passing of information between agents. While other... | 1 | Neural Networks | cora | 1,847 | val |
1-hop neighbor's text information: Applications of a logical discovery engine. : The clausal discovery engine claudien is presented. claudien discovers regularities in data and is a representative of the inductive logic programming paradigm. As such, it represents data and regularities by means of first order clausal ... | 0 | Rule Learning | cora | 2,646 | test |
1-hop neighbor's text information: (1991) Learning Polynomial functions by feature Construction. : We present a method for learning higher-order polynomial functions from examples using linear regression and feature construction. Regression is used on a set of training instances to produce a weight vector for a linear... | 4 | Theory | cora | 254 | test |
1-hop neighbor's text information: Statistical mechanics of nonlinear nonequilibrium financial markets, : The work in progress reported by Wright & Liley shows great promise, primarily because of their experimental and simulation paradigms. However, their tentative conclusion that macroscopic neocortex may be consider... | 1 | Neural Networks | cora | 402 | test |
1-hop neighbor's text information: A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment: Classifying by hand complex data coming from psychology experiments can be a long and difficult task, because of the quantity of data to classify and the amount of training it may re... | 5 | Reinforcement Learning | cora | 2,620 | test |
1-hop neighbor's text information: Bilinear separation of two sets in n-space. : The NP-complete problem of determining whether two disjoint point sets in the n-dimensional real space R n can be separated by two planes is cast as a bilinear program, that is minimizing the scalar product of two linear functions on a po... | 1 | Neural Networks | cora | 790 | test |
1-hop neighbor's text information: Multivariate versus univariate decision trees. : COINS Technical Report 92-8 January 1992 Abstract In this paper we present a new multivariate decision tree algorithm LMDT, which combines linear machines with decision trees. LMDT constructs each test in a decision tree by training a ... | 1 | Neural Networks | cora | 168 | test |
1-hop neighbor's text information: Evolving Artificial Neural Networks using the Baldwin Effect: This paper describes how through simple means a genetic search towards optimal neural network architectures can be improved, both in the convergence speed as in the quality of the final result. This result can be theoretica... | 1 | Neural Networks | cora | 937 | test |
1-hop neighbor's text information: Supervised learning from incomplete data via an EM approach. : Real-world learning tasks may involve high-dimensional data sets with arbitrary patterns of missing data. In this paper we present a framework based on maximum likelihood density estimation for learning from such data set... | 1 | Neural Networks | cora | 1,772 | test |
1-hop neighbor's text information: Resonance and the perception of musical meter. : Many connectionist approaches to musical expectancy and music composition let the question of What next? overshadow the equally important question of When next?. One cannot escape the latter question, one of temporal structure, when co... | 1 | Neural Networks | cora | 495 | train |
1-hop neighbor's text information: Learning hierarchical rule sets. : We present an algorithm for learning sets of rules that are organized into up to k levels. Each level can contain an arbitrary number of rules "if c then l" where l is the class associated to the level and c is a concept from a given class of basic ... | 4 | Theory | cora | 965 | test |
1-hop neighbor's text information: An Information Maximization Approach to Blind Separation and Blind Deconvolution. : We derive a new self-organising learning algorithm which maximises the information transferred in a network of non-linear units. The algorithm does not assume any knowledge of the input distributions,... | 1 | Neural Networks | cora | 135 | test |
1-hop neighbor's text information: Veloso (1994). Planning and Learning by Analogical Reasoning. : Realistic and complex planning situations require a mixed-initiative planning framework in which human and automated planners interact to mutually construct a desired plan. Ideally, this joint cooperation has the potenti... | 2 | Case Based | cora | 786 | test |
1-hop neighbor's text information: Belief Networks Revisited:
1-hop neighbor's text information: (1996c) Feedback Models: Interpretation and Discovery. :
1-hop neighbor's text information: "Aspects of Graphical Models Connected With Causality," : This paper demonstrates the use of graphs as a mathematical tool for ... | 6 | Probabilistic Methods | cora | 1,971 | train |
1-hop neighbor's text information: Sequential Thresholds: Context Sensitive Default Extensions: Default logic encounters some conceptual difficulties in representing common sense reasoning tasks. We argue that we should not try to formulate modular default rules that are presumed to work in all or most circumstances. W... | 6 | Probabilistic Methods | cora | 1,322 | test |
1-hop neighbor's text information: Cliff (1993). "Issues in evolutionary robotics," From Animals to Animats 2 (Ed. : 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 Boo... | 3 | Genetic Algorithms | cora | 1,199 | val |
1-hop neighbor's text information: LEARNING MORE FROM LESS DATA: EXPERIMENTS WITH LIFELONG ROBOT LEARNING:
1-hop neighbor's text information: Is Learning the n-th Thing Any Easier Than Learning the First? in: : This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a l... | 1 | Neural Networks | cora | 1,936 | test |
1-hop neighbor's text information: Improving Bagging Performance by Increasing Decision Tree Diversity: Ensembles of decision trees often exhibit greater predictive accuracy than single trees alone. Bagging and boosting are two standard ways of generating and combining multiple trees. Boosting has been empirically dete... | 4 | Theory | cora | 1,973 | val |
1-hop neighbor's text information: Learning to coordinate without sharing information. : Researchers in the field of Distributed Artificial Intelligence (DAI) have been interested in developing efficient mechanisms to coordinate the activities of multiple autonomous agents. The need for coordination arises because age... | 5 | Reinforcement Learning | cora | 436 | val |
1-hop neighbor's text information: Limits of control flow on parallelism. : This paper discusses three techniques useful in relaxing the constraints imposed by control flow on parallelism: control dependence analysis, executing multiple flows of control simultaneously, and speculative execution. We evaluate these tech... | 0 | Rule Learning | cora | 1,146 | test |
1-hop neighbor's text information: "Learning and evolution in neural networks," :
1-hop neighbor's text information: Evolution of Homing Navigation in a Real Mobile Robot. : In this paper we describe the evolution of a discrete-time recurrent neural network to control a real mobile robot. In all our experiments the ... | 3 | Genetic Algorithms | cora | 2,694 | test |
1-hop neighbor's text information: The ilp description learning problem: Towards a genearl model-leve definition of data mining in ilp. : 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 Rep... | 0 | Rule Learning | cora | 2,409 | test |
1-hop neighbor's text information: Nonlinear Prediction of Chaotic Time Series. : A novel method for regression has been recently proposed by V. Vapnik et al. [8, 9]. The technique, called Support Vector Machine (SVM), is very well founded from the mathematical point of view and seems to provide a new insight in funct... | 1 | Neural Networks | cora | 210 | test |
1-hop neighbor's text information: Data Structures and Genetic Programming, : It is established good software engineering practice to ensure that programs use memory via abstract data structures such as stacks, queues and lists. These provide an interface between the program and memory, freeing the program of memory m... | 3 | Genetic Algorithms | cora | 2,114 | test |
1-hop neighbor's text information: Construction of phylogenetic trees. : 6] Farach, M. and Thorup, M. 1993. Fast Comparison of Evolutionary Trees, Technical Report 93-46, DIMACS, Rutgers University, Piscataway, NJ.
Target text information: A six-point condition for ordinal matrices, : Ordinal assertions in an evoluti... | 4 | Theory | cora | 2,521 | test |
1-hop neighbor's text information: Dynamic Hammock Predication for Non-predicated Instruction Set Architectures: Conventional speculative architectures use branch prediction to evaluate the most likely execution path during program execution. However, certain branches are difficult to predict. One solution to this prob... | 0 | Rule Learning | cora | 1,192 | test |
1-hop neighbor's text information: A simple randomized quantization algorithm for neural network pattern classifiers. : This paper explores some algorithms for automatic quantization of real-valued datasets using thermometer codes for pattern classification applications. Experimental results indicate that a relatively... | 1 | Neural Networks | cora | 1,018 | test |
1-hop neighbor's text information: Empirical Analysis of the General Utility Problem in Machine Learning, : The overfit problem in inductive learning and the utility problem in speedup learning both describe a common behavior of machine learning methods: the eventual degradation of performance due to increasing amount... | 2 | Case Based | cora | 1,584 | test |
1-hop neighbor's text information: Sequential Thresholds: Context Sensitive Default Extensions: Default logic encounters some conceptual difficulties in representing common sense reasoning tasks. We argue that we should not try to formulate modular default rules that are presumed to work in all or most circumstances. W... | 6 | Probabilistic Methods | cora | 1,321 | test |
1-hop neighbor's text information: Scaling Up. : Partially observable Markov decision processes (pomdp's) model decision problems in which an agent tries to maximize its reward in the face of limited and/or noisy sensor feedback. While the study of pomdp's is motivated by a need to address realistic problems, existing... | 5 | Reinforcement Learning | cora | 2,672 | test |
1-hop neighbor's text information: Learning when reformulation is appropriate for iterative design. : It is well known that search-space reformulation can improve the speed and reliability of numerical optimization in engineering design. We argue that the best choice of reformulation depends on the design goal, and pr... | 2 | Case Based | cora | 2,078 | test |
1-hop neighbor's text information: Generating Declarative Language Bias for Top-Down ILP Algorithms: 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,... | 0 | Rule Learning | cora | 1,915 | val |
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