Article

Learning for efficient retrieval of structured data with noisy queries

Authors:

Charles Parker,

Prasad TadepalliAuthors Info & Claims

ICML '07: Proceedings of the 24th international conference on Machine learning

Pages 729 - 736

https://doi.org/10.1145/1273496.1273588

Published: 20 June 2007 Publication History

Abstract

Increasingly large collections of structured data necessitate the development of efficient, noise-tolerant retrieval tools. In this work, we consider this issue and describe an approach to learn a similarity function that is not only accurate, but that also increases the effectiveness of retrieval data structures. We present an algorithm that uses functional gradient boosting to maximize both retrieval accuracy and the retrieval efficiency of vantage point trees. We demonstrate the effectiveness of our approach on two datasets, including a moderately sized real-world dataset of folk music.

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Cited By

Skopal TBustos B(2011)On nonmetric similarity search problems in complex domainsACM Computing Surveys10.1145/1978802.197881343:4(1-50)Online publication date: 18-Oct-2011
https://dl.acm.org/doi/10.1145/1978802.1978813
Bustos BSkopal T(2011)Non-metric similarity search problems in very large collectionsProceedings of the 2011 IEEE 27th International Conference on Data Engineering10.1109/ICDE.2011.5767955(1362-1365)Online publication date: 11-Apr-2011
https://dl.acm.org/doi/10.1109/ICDE.2011.5767955
Lampert CBlaschko M(2009)Structured prediction by joint kernel support estimationMachine Language10.1007/s10994-009-5111-077:2-3(249-269)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.1007/s10994-009-5111-0
Show More Cited By

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cover image ACM Other conferences

ICML '07: Proceedings of the 24th international conference on Machine learning

June 2007

1233 pages

ISBN:9781595937933

DOI:10.1145/1273496

Editor:
Zoubin Ghahramani
University of Cambridge, United Kingdom

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Machine Learning Journal

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2007

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ICML '07 & ILP '07

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ICML '07 & ILP '07: The 24th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 20 - 24, 2007

Oregon, Corvalis, USA

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Cited By

Skopal TBustos B(2011)On nonmetric similarity search problems in complex domainsACM Computing Surveys10.1145/1978802.197881343:4(1-50)Online publication date: 18-Oct-2011
https://dl.acm.org/doi/10.1145/1978802.1978813
Bustos BSkopal T(2011)Non-metric similarity search problems in very large collectionsProceedings of the 2011 IEEE 27th International Conference on Data Engineering10.1109/ICDE.2011.5767955(1362-1365)Online publication date: 11-Apr-2011
https://dl.acm.org/doi/10.1109/ICDE.2011.5767955
Lampert CBlaschko M(2009)Structured prediction by joint kernel support estimationMachine Language10.1007/s10994-009-5111-077:2-3(249-269)Online publication date: 1-Dec-2009
https://dl.acm.org/doi/10.1007/s10994-009-5111-0
Dietterich TDomingos PGetoor LMuggleton STadepalli P(2008)Structured machine learningMachine Language10.1007/s10994-008-5079-173:1(3-23)Online publication date: 1-Oct-2008
https://dl.acm.org/doi/10.1007/s10994-008-5079-1

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