Query by Committee made real

Ran Gilad-Bachrach, Amir Navot, Naftali Tishby

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Number of pages8
StatePublished - 2005
Externally publishedYes
Event2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada
Duration: 5 Dec 20058 Dec 2005

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
CityVancouver, BC


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