Retrieval of the top N matches with Support Vector Machines

Jae Jin Kim, Bon Woo Hwang, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Support Vector Machines(SVMs) have been recently proposed for pattern recognition. Their basic property allows us to find a decision surface between two classes in terms of a hyperplane in a high dimensional space. In a multi-class recognition problem, SVMs are used in the form of a combination of binary classifiers. However, SVMs are unable to retrieve the top N matches, since they are designed to yield only one - the best match - in a multi-class problem. In other words, there is no proper similarity measurement for ordering all the classes in a given space using SVMs. In this paper, we present an efficient method for the retrieval of the top N matches in a multi-class problem using SVMs. For evaluation of the proposed method, we compared its result with that of a PCA algorithm in ranking the matches between classes.

Original languageEnglish
Pages (from-to)716-719
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Issue number2
Publication statusPublished - 2000

Bibliographical note

Funding Information:
'This research was supported by Creative Research Initiatives of the Ministry of Science and Technology, Korea.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Retrieval of the top N matches with Support Vector Machines'. Together they form a unique fingerprint.

Cite this