#### Filter Results:

- Full text PDF available (33)

#### Publication Year

1999

2017

- This year (6)
- Last 5 years (31)
- Last 10 years (43)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- 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)

- S. Sathiya Keerthi, Shirish K. Shevade, Chiranjib Bhattacharyya, K. R. K. Murthy
- IEEE Trans. Neural Netw. Learning Syst.
- 2000

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,… (More)

- Shirish K. Shevade, S. Sathiya Keerthi, Chiranjib Bhattacharyya, K. R. K. Murthy
- IEEE Trans. Neural Netw. Learning Syst.
- 2000

This paper points out an important source of inefficiency in Smola and Schölkopf's sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression 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… (More)

- Shirish K. Shevade, S. Sathiya Keerthi
- Bioinformatics
- 2003

MOTIVATION
This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss-Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied… (More)

- S. Sathiya Keerthi, Kaibo Duan, Shirish K. Shevade, Aun Neow Poo
- Machine Learning
- 2002

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The… (More)

- S S Keerthi, S K Shevade
- Neural computation
- 2003

This article extends the well-known SMO algorithm of support vector machines (SVMs) to least-squares SVM formulations that include LS-SVM classification, kernel ridge regression, and a particular form of regularized kernel Fisher discriminant. The algorithm is shown to be asymptotically convergent. It is also extremely easy to implement. Computational… (More)

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to computational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classification. In… (More)

- S K Shevade, S S Keerthi, C Bhattacharyya, K R K Murthy
- 1999

This paper points out an important source of ine ciency in Smola and Sch olkopf's Sequential Minimal Optimization (SMO) algorithm for SVM regression 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 modi cations of SMO for regression. These modi… (More)

- S. Asharaf, M. Narasimha Murty, Shirish K. Shevade
- ICML
- 2007

Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle large data sets still needs much of exploration. Core Vector Machine(CVM) is a technique for scaling up a two class SVM to handle large data sets. In this paper we… (More)

- S. Asharaf, M. Narasimha Murty, Shirish K. Shevade
- Pattern Recognition Letters
- 2006