PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Optimal single-class classification strategies
Ran El-Yaniv and Mordechai Nisenson
In: NIPS 2006, 4-7 Dec 2006, Vancouver, Canada.

Abstract

We consider single-class classification (SCC) as a two-person game between the learner and an adversary. In this game the target distribution is completely known to the learner and the learner's goal is to construct a classifier capable of guaranteeing a given tolerance for the false-positive error while minimizing the false negative error. We identify both ``hard'' and ``soft'' optimal classification strategies for different types of games and demonstrate that soft classification can provide a significant advantage. Our optimal strategies and bounds provide worst-case lower bounds for standard, finite-sample SCC and also motivate new approaches to solving SCC.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2663
Deposited By:Mordechai Nisenson
Deposited On:22 November 2006