Learning with support vector machines for query-by-multiple-examples

[DBLP_Link] [Online_Version] CitedBy 1

Abstract:

We explore an alternative Information Retrieval paradigm called Query-By-Multiple-Examples (QBME) where the information need is described not by a set of terms but by a set of documents. Intuitive ideas for QBME include using the centroid of these documents or the well-known Rocchio algorithm to construct the query vector. We consider this problem from the perspective of text classification, and find that a better query vector can be obtained through learning with Support Vector Machines (SVMs). For online queries, we show how SVMs can be learned from one-class examples in linear time. For offline queries, we show how SVMs can be learned from positive and unlabeled examples together in linear or polynomial time. The effectiveness and efficiency of the proposed approaches have been confirmed by our experiments on four real-world datasets.
Year: 2008
Pages: 2
In Proceedings: SIGIR

Authors:

Dell Zhang

(Assistant Professor, School of Computer Science and Information Systems (SCSIS))

H-index: 11; Papers: 27; Citation: 605 [FOAF]

Homepage: http://www.dcs.bbk.ac.uk/~Dell/

Expertise: Information Retrieval / Probabilistic Indexing; Information Systems Design / Micro computer software; Data Mining / Query Processing;

Wee Sun Lee

(Associate Professor, Department of Computer Science Computing 1 National University of Singapore)

H-index: 18; Papers: 53; Citation: 2494 [FOAF]

Homepage: http://www.comp.nus.edu.sg/~leews/

Expertise: Machine Learning; Information Retrieval / Probabilistic Indexing; Data Compression / Arithmetic Coding; Web Mining; Learning Search Control Rules / Explanation-based Approach;

Reference:  [Top]

Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Authors: Bing Liu

Introduction to Information Retrieval
Authors: Christopher D. Manning     Prabhakar Raghavan     Hinrich Schtze

A support vector method for multivariate performance measures
Authors: Thorsten Joachims
Organization: ICML

Training linear SVMs in linear time
Authors: Thorsten Joachims
Organization: KDD

Dell Zhang, Wee Sun Lee. Learning with support vector machines for query-by-multiple-examples. In Proceedings of SIGIR'2008. pp.835~836

Other Format:

LNCS: [Top]
Zhang, D., Lee, W.S.: Learning with support vector machines for query-by-multiple-examples. In SIGIR(2008) 835-836

IEEE:
D. Zhang and W.S. Lee, "Learning with support vector machines for query-by-multiple-examples", in Proc. SIGIR, 2008, pp.835-836.

ACM:
Zhang, D. and Lee, W.S. Learning with support vector machines for query-by-multiple-examples. In Proceedings of SIGIR. 2008, 835-836.

Latex:
@INPROCEEDINGS{ zhang:learning, AUTHOR = "Dell Zhang and Wee Sun Lee", TITLE = "Learning with support vector machines for query-by-multiple-examples", booktitle = "SIGIR'08", PAGES = {835-836}, YEAR = {2008} }

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