Paper Info
Title
Retrieval Of The Top N Matches With Support Vector Machines
Abstract
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.
Year
DOI
Venue
2000
10.1109/ICPR.2000.906175
15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS
Keywords
Field
DocType
svm,principal component analysis,face detection,pattern matching,database management systems,information retrieval,hyperplane,pattern recognition,decision surface,space technology,support vector machines,computational complexity,support vector machine
Structured support vector machine,Ranking SVM,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Hyperplane,Decision boundary,Pattern matching,Computational complexity theory,Multiclass classification
Conference
ISSN
Citations 
PageRank 
1051-4651
4
0.53
References 
Authors
4
3
Name
Order
Citations
PageRank
Jae-Jin Kim16713.37
Bon-Woo Hwang217716.33
Seong-Whan Lee33756343.90