PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Query by Committee Made Real
Ran Gilad-Bachrach, Amir Navot and Naftali Tishby
Advances in Neural Information Processing Systems (NIPS) Volume 19, 2005.


Training a learning algorithm is a costly task. A major goal of active learning is to reduce this cost. In this paper we introduce a new algorithm, KQBC, which is capable of actively learning large scale problems by using selective sampling. The algorithm overcomes the costly sampling step of the well known Query By Committee (QBC) algorithm by projecting onto a low dimensional space. KQBC also enables the use of kernels, providing a simple way of extending QBC to the non-linear scenario. Sampling the low dimension space is done using the hit and run random walk. We demonstrate the success of this novel algorithm by applying it to both artificial and a real world problems

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2004
Deposited By:Naftali Tishby
Deposited On:14 January 2006