PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Stable transductive learning
Ran El-Yaniv and Dmitry Pechyony
In: COLT 2006, 22-25 June 2006, Pittsburgh, USA.

Abstract

We develop a new error bound for transductive learning algorithms. The slack term in the new bound is a function of a relaxed notion of \emph{transductive stability}, which measures the sensitivity of the algorithm to most pairwise exchanges of training and test set points. Our bound is based on a novel concentration inequality for symmetric functions of permutations. We also present a simple sampling technique that can estimate, with high probability, the weak stability of transductive learning algorithms with respect to a given dataset. We demonstrate the usefulness of our estimation technique on a well known transductive learning algorithm.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:2611
Deposited By:Dmitry Pechyony
Deposited On:22 November 2006