Please use this identifier to cite or link to this item:
Title: A fast iterative nearest point algorithm for support vector machine classifier design
Authors: Keerthi, S.S. 
Shevade, S.K.
Bhattacharyya, C.
Murthy, K.R.K.
Keywords: Classification
Nearest point algorithm
Quadratic programming
Support vector machine
Issue Date: Jan-2000
Citation: Keerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K. (2000-01). A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Transactions on Neural Networks 11 (1) : 124-136. ScholarBank@NUS Repository.
Abstract: In this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell et al., is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and Frieß is used to convert it to a problem in which there are no classification violations. Comparative computational evaluation of our algorithm against powerful SVM methods such as Platt's sequential minimal optimization shows that our algorithm is very competitive. © 2000 IEEE.
Source Title: IEEE Transactions on Neural Networks
ISSN: 10459227
DOI: 10.1109/72.822516
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Oct 13, 2021


checked on Oct 13, 2021

Page view(s)

checked on Oct 14, 2021

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.