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- S. Sathiya Keerthi, Shirish K. Shevade, Chiranjib Bhattacharyya, K. R. K. Murthy
- Neural Computation
- 2001

This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKTâ€¦ (More)

In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such large-scale sparse data.â€¦ (More)

- S. Sathiya Keerthi, Chih-Jen Lin
- Neural Computation
- 2003

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and theâ€¦ (More)

- Elmer G. Gilbert, Daniel W. Johnson, S. Sathiya Keerthi
- IEEE J. Robotics and Automation
- 1988

An efficient and reliable algorithm for computing the Euclidean distance between a pair of convex sets in Rm is described. Extensive numerical experience with a broad family of polytopes in R 3 showsâ€¦ (More)

- Wei Chu, S. Sathiya Keerthi
- Neural Computation
- 2007

In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Bothâ€¦ (More)

- Olivier Chapelle, S. Sathiya Keerthi
- Information Retrieval
- 2009

RankSVM (Herbrich etÂ al. in Advances in large margin classifiers. MIT Press, Cambridge, MA, 2000; Joachims in Proceedings of the ACM conference on knowledge discovery and data mining (KDD), 2002) isâ€¦ (More)

- Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi
- Journal of Machine Learning Research
- 2008

Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs) are based onâ€¦ (More)

- Wei Chu, S. Sathiya Keerthi
- ICML
- 2005

In this paper, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Bothâ€¦ (More)

- Vikas Sindhwani, S. Sathiya Keerthi
- SIGIR
- 2006

Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In many informationâ€¦ (More)

- S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste
- Journal of Machine Learning Research
- 2006

Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem weâ€¦ (More)