PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning to Learn with the Informative Vector Machine
Neil Lawrence and John Platt
In: ICML 2004, July 4-8 2004, Banff, Canada.


This paper describes an efficient method for learning the parameters of a Gaussian process (GP). The parameters are learned from multiple tasks which are assumed to have been drawn independently from the same GP prior. An efficient algorithm is obtained by extending the informative vector machine (IVM) algorithm to handle the multi-task learning case. The multi-task IVM (MT-IVM) saves computation by greedily selecting the most informative examples from the separate tasks. The MT-IVM is also shown to be more efficient than sub-sampling on an artificial data-set and more effective than the traditional IVM in a speaker dependent phoneme recognition task.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Theory & Algorithms
ID Code:913
Deposited By:Neil Lawrence
Deposited On:06 January 2005