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ISBN:1558604197
Author: Author Unknown
ISBN13: 978-1558604193
Title: Machine Learning Proceedings 1996
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ePUB size: 1139 kb
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Language: English
Category: Computer Science
Publisher: Morgan Kaufmann (July 15, 1996)
Pages: 700

Machine Learning Proceedings 1996 by Author Unknown



This book constitutes the refereed post-conference proceedings of the Second International Andrei Ershov Memorial Conference on System Informatics, held in Akademgorodok, Novosibirsk, Russia, in June 1996. The 27 revised full papers presented together with 9 invited contributions were thoroughly refereed for inclusion in this volume

Proceedings of the European conference on machine learning, Pisa, Italy Berlin: Springer-Verlag; 2004;63–74. 17. Baldi P, Hornik K. Neural networks and principal component analysis: Learning from examples without local minima. Proceedings of the 23rd international conference on machine learning New York, NY: ACM Press; 2006;97–104. 34. Bifet A, Holmes G, Kirkby R, Pfahringer B. MOA: Massive online analysis.

Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions  . Ali, . & Pazzani, . Error reduction through learning multiple descriptions. Machine Learning, 24(3), 173–202. Bauer, . & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1/2), 105–139. CrossRefGoogle Scholar. Blum, . & Rivest, . Training a 3-node neural network is NP-Complete (Extended abstract). In Proceedings of the 1988 Workshop on Computational Learning Theory, pp. 9–18 San Francisco, CA. Morgan Kaufmann.

The areas of On-Line Algorithms and Machine Learning are both concerned with problems of making decisions about the present based only on knowledge of the past. Although these areas differ in terms of their emphasis and the problems typically studied, there are a collection of results in Computational Learning Theory that fit nicely into the "on-line algorithms" framework. This survey article discusses some of the results, models, and open problems from Computational Learning Theory that seem particularly interesting from the point of view of on-line algorithms. The emphasis in this. In Proceedings of the Workshop on On-Line Algorithms, Dagstuhl.

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Reinforcement learning with replacing eligibility traces. Machine learning 22 (1-3), 123-158, 1996. Convergence of stochastic iterative dynamic programming algorithms. T Jaakkola, MI Jordan, SP Singh. Transfer of learning by composing solutions of elemental sequential tasks. Machine Learning 8 (3-4), 323-339, 1992.

In Machine Learning: Proceedings of the Thirteenth International Conference 148-156. Morgan Kaufmann, San Francisco. Greedy predictive approximation: a gradient boosting machine. Technical report, Dept. Statistics Stanford Univ. Michie, . Spiegelhalter, D. and Tay lor, C. (1994). Machine Learning, Neural and Statistical Classification. Ellis Horwood, New York. Mosteller, F. and Tukey, J. (1977).

Learning classifier systems. Complex Adaptive Systems series. Topics include: Applications of genetic programming.

In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. This paper describes various supervised machine learning classification techniques.