PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An Algorithm for Transfer Learning in a Heterogeneous Environment
Andreas Maurer and Massimiliano Pontil
In: ECML, Antwerp(2008).

Abstract

We consider the problem of learning in an environment of classification tasks. Tasks sampled from the environment are used to improve classification performance on future tasks. We consider situations in which the tasks can be divided into groups. Tasks within each group are related by sharing a low dimensional representation, which differs across the groups. We present an algorithm which divides the sampled tasks into groups and computes a common representation for each group. We report experiments on a synthetic and two image data sets, which show the advantage of the approach over single-task learning and a previous transfer learning method.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:4729
Deposited By:Andreas Argyriou
Deposited On:24 March 2009