Open AccessProceedings Article
Learning Sparse Perceptrons
Jeffrey C. Jackson,Mark Craven +1 more
- 27 Nov 1995
- Vol. 8, pp 654-660
TL;DR: A new algorithm designed to learn sparse perceptrons over input representations which include high-order features is introduced, which is based on a hypothesis-boosting method and is able to PAC-learn a relatively natural class of target concepts.
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Abstract: We introduce a new algorithm designed to learn sparse perceptrons over input representations which include high-order features. Our algorithm, which is based on a hypothesis-boosting method, is able to PAC-learn a relatively natural class of target concepts. Moreover, the algorithm appears to work well in practice: on a set of three problem domains, the algorithm produces classifiers that utilize small numbers of features yet exhibit good generalization performance. Perhaps most importantly, our algorithm generates concept descriptions that are easy for humans to understand.
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Citations
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
- 01 Aug 1997
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
•Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
- 03 Jul 1996
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
A Short Introduction to Boosting
Yoav Freund,Robert E. Schapire +1 more
- 01 Jan 1999
TL;DR: This short overview paper introduces the boosting algorithm AdaBoost, and explains the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting as well as boosting’s relationship to support-vector machines.
The Boosting Approach to Machine Learning An Overview
Robert E. Schapire
- 01 Jan 2003
TL;DR: This chapter overviews some of the recent work on boosting including analyses of AdaBoost's training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of Ada boost for multiclass classification problems; methods of incorporating human knowledge into boosting; and experimental and applied work using boosting.
References
•Book
C4.5: Programs for Machine Learning
J. Ross Quinlan
- 15 Oct 1992
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
27.2K
Learning internal representations by error propagation
David E. Rumelhart,Geoffrey E. Hinton,Ronald J. Williams +2 more
- 01 Jan 1988
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
- 01 Aug 1997
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
David E. Rumelhart,James L. McClelland,Au +2 more
- 17 Jul 1986
TL;DR: The fundamental principles, basic mechanisms, and formal analyses involved in the development of parallel distributed processing (PDP) systems are presented in individual chapters contributed by leading experts.
16.7K
A theory of the learnable
Leslie G. Valiant
- 05 Nov 1984
TL;DR: This paper regards learning as the phenomenon of knowledge acquisition in the absence of explicit programming, and gives a precise methodology for studying this phenomenon from a computational viewpoint.
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