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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 KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO. These modified algorithms perform significantly faster… (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. This paper presents a novel dual coordinate descent method for linear SVM with L1-and L2-loss functions. The proposed method is simple and reaches an… (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 shows that the computational cost is approximately linear in the total number of vertices specifying the two polytopes. The algorithm has special features which… (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 hyperparameters: the penalty parameter C and the kernel width sigma. This letter analyzes the behavior of the SVM classifier when these hyperparameters take very small or very large values. Our results help in… (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. <i>Both approaches guarantee that the thresholds are properly ordered at the optimal solution.</i> The size of these optimization problems is linear in the number of… (More)

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

RankSVM (Herbrich et 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) is a pairwise method for designing ranking models. SVMLight is the only publicly available software for RankSVM. It is slow and, due to incomplete training with… (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 information retrieval and data mining applications, linear classifiers are strongly preferred because of their ease of implementation, interpretability and empirical… (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. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training… (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 on applying the margin maximization principle to both labeled and unlabeled examples. Unlike SVMs, their formulation leads to a non-convex optimization problem. A suite… (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 we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a… (More)