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10,898
EEGLAB: an open source toolbox for analysis of singletrial EEG dynamics including independent component analysis
 J. Neurosci. Methods
"... Abstract: We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of singletrial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event i ..."
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Cited by 886 (45 self)
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Abstract: We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of singletrial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event
MetaCost: A General Method for Making Classifiers CostSensitive
 In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining
, 1999
"... Research in machine learning, statistics and related fields has produced a wide variety of algorithms for classification. However, most of these algorithms assume that all errors have the same cost, which is seldom the case in KDD prob lems. Individually making each classification learner costsensi ..."
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Cited by 415 (4 self)
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functioning or change to it. Unlike stratification, MetaCost is applicable to any number of classes and to arbitrary cost matrices. Empirical trials on a large suite of benchmark databases show that MetaCost almost always produces large cost reductions compared to the costblind classifier used (C4.5RULES
Learning from demonstration”.
 Advances in Neural Information Processing Systems 9.
, 1997
"... Abstract By now it is widely accepted that learning a task from scratch, i.e., without any prior knowledge, is a daunting undertaking. Humans, however, rarely attempt to learn from scratch. They extract initial biases as well as strategies how to approach a learning problem from instructions and/or ..."
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Cited by 399 (32 self)
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/or demonstrations of other humans. For learning control, this paper investigates how learning from demonstration can be applied in the context of reinforcement learning. We consider priming the Qfunction, the value function, the policy, and the model of the task dynamics as possible areas where demonstrations can
OnLine QLearning Using Connectionist Systems
, 1994
"... Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete finitestate Markovian problems, which is too restrictive for many realworld environments. Therefore, it is desirable to extend these ..."
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Cited by 381 (1 self)
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these methods to high dimensional continuous statespaces, which requires the use of function approximation to generalise the information learnt by the system. In this report, the use of backpropagation neural networks (Rumelhart, Hinton and Williams 1986) is considered in this context. We consider a number
Automatic Programming of Behaviorbased Robots using Reinforcement Learning
, 1991
"... This paper describes a general approach for automatically programming a behaviorbased robot. New behaviors are learned by trial and error using a performance feedback function as reinforcement. Two algorithms for behavior learning are described that combine Q learning, a well known scheme for propa ..."
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Cited by 368 (17 self)
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This paper describes a general approach for automatically programming a behaviorbased robot. New behaviors are learned by trial and error using a performance feedback function as reinforcement. Two algorithms for behavior learning are described that combine Q learning, a well known scheme
A new meshless local PetrovGalerkin (MLPG) approach in computational mechanics
 Comput. Mech
, 1998
"... Abstract: A comparison study of the efficiency and accuracy of a variety of meshless trial and test functions is presented in this paper, based on the general concept of the meshless local PetrovGalerkin (MLPG) method. 5 types of trial functions, and 6 types of test functions are explored. Differe ..."
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Cited by 312 (54 self)
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Abstract: A comparison study of the efficiency and accuracy of a variety of meshless trial and test functions is presented in this paper, based on the general concept of the meshless local PetrovGalerkin (MLPG) method. 5 types of trial functions, and 6 types of test functions are explored
The adaptive nature of human categorization
 Psychological Review
, 1991
"... A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partiti ..."
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Cited by 344 (2 self)
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of linearly nonseparable categories, effects of category labels, extraction of basic level categories, baserate effects, probability matching in categorization, and trialbytrial learning functions. Although the rational model considers just I level of categorization, it is shown how predictions can
Matching as Nonparametric Preprocessing for Reducing Model Dependence
 in Parametric Causal Inference,” Political Analysis
, 2007
"... Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other ..."
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Cited by 334 (46 self)
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Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms
Quest: a bayesian adaptive psychometric method
 PERCEPT PSYCHOPHYS
, 1983
"... An adaptive psychometric procedure that places each trial at the current most probable Baye& ian estimate of threshold is described. The procedure takes advantage of the common finding that the human psychometric function is invariant in form when expressed as a function of log intensity. The pr ..."
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Cited by 321 (25 self)
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An adaptive psychometric procedure that places each trial at the current most probable Baye& ian estimate of threshold is described. The procedure takes advantage of the common finding that the human psychometric function is invariant in form when expressed as a function of log intensity
Analysis of fMRI Data by Blind Separation Into Independent Spatial Components
 HUMAN BRAIN MAPPING 6:160–188(1998)
, 1998
"... Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent comp ..."
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Cited by 317 (18 self)
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Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent
Results 1  10
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10,898