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Journal of Machine Learning Research 9 (2008) 285-312 Submitted 2/07; Revised 9/07; Published 2/08 Support Vector Machinery for Infinite Ensemble Learning
 

Summary: Journal of Machine Learning Research 9 (2008) 285-312 Submitted 2/07; Revised 9/07; Published 2/08
Support Vector Machinery for Infinite Ensemble Learning
Hsuan-Tien Lin htlin@caltech.edu
Ling Li ling@caltech.edu
Department of Computer Science
California Institute of Technology
Pasadena, CA 91125, USA
Editor: Peter L. Bartlett
Abstract
Ensemble learning algorithms such as boosting can achieve better performance by averag-
ing over the predictions of some base hypotheses. Nevertheless, most existing algorithms
are limited to combining only a finite number of hypotheses, and the generated ensemble
is usually sparse. Thus, it is not clear whether we should construct an ensemble classifier
with a larger or even an infinite number of hypotheses. In addition, constructing an infi-
nite ensemble itself is a challenging task. In this paper, we formulate an infinite ensemble
learning framework based on the support vector machine (SVM). The framework can out-
put an infinite and nonsparse ensemble through embedding infinitely many hypotheses into
an SVM kernel. We use the framework to derive two novel kernels, the stump kernel and
the perceptron kernel. The stump kernel embodies infinitely many decision stumps, and the
perceptron kernel embodies infinitely many perceptrons. We also show that the Laplacian

  

Source: Abu-Mostafa, Yaser S. - Department of Mechanical Engineering & Computer Science Department, California Institute of Technology

 

Collections: Computer Technologies and Information Sciences