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.
Nearest point algorithm
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. https://doi.org/10.1109/72.822516||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/54135||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 Mar 7, 2021
WEB OF SCIENCETM
checked on Feb 26, 2021
checked on Mar 2, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.