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Unsupervised learning of finite mixture models

by Mario A. T. Figueiredo, Anil K. Jain - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2002
"... This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM) alg ..."
Abstract - Cited by 418 (22 self) - Add to MetaCart
This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective ªunsupervisedº is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM

Toward Optimal Active Learning through Sampling Estimation of Error Reduction

by Nicholas Roy, Andrew Mccallum - In Proc. 18th International Conf. on Machine Learning , 2001
"... This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation of the expec ..."
Abstract - Cited by 353 (2 self) - Add to MetaCart
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce version space size. These other methods are popular because for many learning models, closed form calculation

Steps Toward an Ecology of Infrastructure: Design and Access for Large Information Spaces

by Susan Leigh Star, Karen Ruhleder - Information Systems Research , 1996
"... We analyze a large-scale custom software effort, the Worm Community system (WCS), a collaborative system designed for a geographically dispersed community of geneticists. There were complex challenges in creating this infrastructural tool, ranging from simple lack of resources to complex organizatio ..."
Abstract - Cited by 310 (2 self) - Add to MetaCart
in the Internet and its utilities (1991-1994), and many respondents turned to the World Wide Web for their information exchange. Using Bateson’s model of levels of learning, we analyze the levels of infrastructural complexity involved in system access and designeruser communication. We analyze the connection

Toward efficient agnostic learning

by Michael J. Kearns, Robert E. Schapire, Linda M. Sellie, Lisa Hellerstein - In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory , 1992
"... Abstract. In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make virtua ..."
Abstract - Cited by 231 (8 self) - Add to MetaCart
Abstract. In this paper we initiate an investigation of generalizations of the Probably Approximately Correct (PAC) learning model that attempt to significantly weaken the target function assumptions. The ultimate goal in this direction is informally termed agnostic learning, in which we make

Fields of experts: A framework for learning image priors

by Stefan Roth, Michael J. Black - In CVPR , 2005
"... We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov Random Field (MRF) models by learning potential functions over extended pixel neighborhood ..."
Abstract - Cited by 292 (4 self) - Add to MetaCart
We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The approach extends traditional Markov Random Field (MRF) models by learning potential functions over extended pixel

Towards a modern theory of adaptive networks: expectation and prediction

by Richard S. Sutton, Andrew G. Barto - Psychol. Rev , 1981
"... Many adaptive neural network theories are based on neuronlike adaptive elements that can behave as single unit analogs of associative conditioning. In this article we develop a similar adaptive element, but one which is more closely in accord with the facts of animal learning theory than elements co ..."
Abstract - Cited by 282 (18 self) - Add to MetaCart
in firing B is increased, is the most familiar of these postulates (Hebb, 1949). This rule for synaptic plasticity is a neural analog of associative conditioning and continues to exert a powerful influence on theoretical and experimental research in learning and memory. Neural network models designed

Hierarchical Bayesian Inference in the Visual Cortex

by Tai Sing Lee, David Mumford , 2002
"... this paper, we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern the- ory and on (b) recent neurophysiological experimental evidence. We ,2 have proposed that Grenander's pattern theory 3 coul ..."
Abstract - Cited by 300 (2 self) - Add to MetaCart
could potentially model the brain as a generafive model in such a way that feedback serves to disambiguate and 'explain away' the earlier representa- tion. The Helmholtz machine 4, 5 was an excellent step towards approximating this proposal, with feedback implementing priors. Its development

Learning and Sequential Decision Making

by Andrew G. Barto, R. S. Sutton, C. J. C. H. Watkins - LEARNING AND COMPUTATIONAL NEUROSCIENCE , 1989
"... In this report we show how the class of adaptive prediction methods that Sutton called "temporal difference," or TD, methods are related to the theory of squential decision making. TD methods have been used as "adaptive critics" in connectionist learning systems, and have been pr ..."
Abstract - Cited by 205 (11 self) - Add to MetaCart
associative strengths in behavioral models, or connection weights in connectionist networks. Because this report is oriented primarily toward the non-engineer interested in animal learning, it presents tutorials on stochastic sequential decision tasks, stochastic dynamic programming, and parameter estimation.

Constructive Incremental Learning From Only Local Information

by Stefan Schaal, Christopher G. Atkeson - NEURAL COMPUTATION
"... We introduce a constructive, incremental learning system for regression problems that models data by means of spatially localized linear models. In contrast to other approaches, the size and shape of the receptive field of each locally linear model as well as the parameters of the locally linear mod ..."
Abstract - Cited by 208 (40 self) - Add to MetaCart
while dealing with the bias-variance dilemma in a principled way. The spatial localization of the linear models increases robustness towards negative interference. Our learning system can be interpreted as a nonparametric adaptive bandwidth smoother, as a mixture of experts where the experts are trained

Word-of-mouth learning

by Abhijit Banerjee , Drew Fudenberg , 2004
"... This paper analyzes a model of rational word-of-mouth learning, in which successive generations of agents make once-and-for-all choices between two alternatives. Before making a decision, each new agent samples N old ones and asks them which choice they used and how satisfied they were with it. If ( ..."
Abstract - Cited by 198 (0 self) - Add to MetaCart
This paper analyzes a model of rational word-of-mouth learning, in which successive generations of agents make once-and-for-all choices between two alternatives. Before making a decision, each new agent samples N old ones and asks them which choice they used and how satisfied they were with it
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