PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning SVMs from Sloppily Labeled Data
Guillaume Stempfel and Liva Ralaivola
In: ICANN 09, 14-17 Sept 2009, Cyprus.

Abstract

This paper proposes a modelling of Support Vector Machine (SVM) learning to address the problem of learning with sloppy labels. In binary classification, learning with sloppy labels is the situation where a learner is provided with labelled data, where the observed labels of each class are possibly noisy (flipped) version of their true class and where the probability of flipping a label y to –y only depends on y. The noise probability is therefore constant and uniform within each class: learning with positive and unlabeled data is for instance a motivating example for this model. In order to learn with sloppy labels, we propose SloppySvm, an SVM algorithm that minimizes a tailored nonconvex functional that is shown to be a uniform estimate of the noise-free SVM functional. Several experiments validate the soundness of our approach.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6671
Deposited By:Liva Ralaivola
Deposited On:08 March 2010