PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

n the Complexity of Good Samples for Learning
Joel Ratsaby
Proc. Tenth International Computing and Combinatorics Conference(COCOON 2004), Jeju Island, Korea Volume Vol. 3106 of Lecture Notes in Computer Science, Springer-Verlag, pp. 198-209, 2004.

Abstract

In machine-learning, maximizing the sample margin can reduce the learning generalization-error. Thus samples on which the target function has a large margin ($\gamma$) convey more information so we expect fewer such samples. In this paper, we estimate the complexity of a class of sets of large-margin samples for a general learning problem over a finite domain. We obtain an explicit dependence of this complexity on $\gamma$ and the sample size.

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Additional Information:Future updated versions of this work are available at http:/www.bgu.ac.il/~ratsaby/Publications.htm
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:808
Deposited By:Joel Ratsaby
Deposited On:30 December 2004