PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Generalizing from several related classification tasks to a new unlabeled sample
Gilles Blanchard, Gyemin Lee and Clayton Scott
Neural Information Processing Systems (NIPS) 2011.

Abstract

We consider the problem of assigning class labels to an unlabeled test data set, given several labeled training data sets drawn from similar distributions. This problem arises in several applications where data distributions fluctuate because of biological, technical, or other sources of variation. We develop a distribution-free, kernel-based approach to the problem. This approach involves identifying an appropriate reproducing kernel Hilbert space and optimizing a regularized empirical risk over the space. We present generalization error analysis, describe universal kernels, and establish universal consistency of the proposed methodology. Experimental results on flow cytometry data are presented.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8698
Deposited By:Gilles Blanchard
Deposited On:20 February 2012