Rough set based incremental clustering of interval data (2006)
Asharaf, S, Murty, Narasimha M, Shevade, SK
This paper introduces a novel incremental approach to clustering interval data. The method employs rough set theory to capture the inherent uncertainty involved in cluster analysis. Our experimental...
Scalable Rough Support Vector Clustering (2006)
Asharaf, S, Shevade, SK, Murty, Narasimha M
In this paper a novel scalable soft support vector clustering algorithm is proposed. Here softness is imparted to Support Vector Clustering paradigm by employing rough set theory and scalability is...
Rough set based incremental clustering of interval data (2006)
Asharaf, S, Murty, Narasimha M, Shevade, SK
This paper introduces a novel incremental approach to clustering interval data. The method employs rough set theory to capture the inherent uncertainty involved in cluster analysis. Our experimental...
Scalable Rough Support Vector Clustering (2006)
Asharaf, S, Shevade, SK, Murty, Narasimha M
In this paper a novel scalable soft support vector clustering algorithm is proposed. Here softness is imparted to Support Vector Clustering paradigm by employing rough set theory and scalability is...
Rough support vector clustering (2005)
Asharaf, S, Shevade, SK, Murty, Narasimha M
In this paper a novel kernel-based soft clustering method is proposed. This method incorporates rough set theoretic flavour in support vector clustering paradigm to achieve soft clustering....
Rough support vector clustering (2005)
Asharaf, S, Shevade, SK, Murty, Narasimha M
In this paper a novel kernel-based soft clustering method is proposed. This method incorporates rough set theoretic flavour in support vector clustering paradigm to achieve soft clustering....
Predictive Approaches for Sparse Model Learning (2004)
Shevade, SK, Sundararajan, S, Keerthi, SS
In this paper we investigate cross validation and Geisser’s sample reuse approaches for designing linear regression models. These approaches generate sparse models by optimizing multiple smoothing...
Predictive Approaches for Sparse Model Learning (2004)
Shevade, SK, Sundararajan, S, Keerthi, SS
In this paper we investigate cross validation and Geisser’s sample reuse approaches for designing linear regression models. These approaches generate sparse models by optimizing multiple smoothing...
A simple and efficient algorithm for gene selection using sparse logistic regression (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....
A simple and efficient algorithm for gene selection using sparse logistic regression (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....
Improvements to Platt's SMO algorithm for SVM classifier design (2001)
Keerthi, SS, Shevade, SK, Bhattacharyya, C, Murthy, KRK
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...
Improvements to the SMO algorithm for SVM regression (2000)
Shevade, SK, Keerthi, SS, Bhattacharyya, C, Murthy, KRK
This paper points out an important source of inefficiency in Smola and Scholkopfs sequential minimal optimization (SMO) algorithm for support vector machine (SVM)regression that is caused by the use...
A Fast Iterative Nearest Point Algorithm for Support Vector Machine Classifier Design (2000)
Keerthi, SS, Shevade, SK, Bhattacharyya, C, Murthy, KRK
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...
Improvements to the SMO algorithm for SVM regression (2000)
Shevade, SK, Keerthi, SS, Bhattacharyya, C, Murthy, KRK
This paper points out an important source of inefficiency in Smola and Scholkopfs sequential minimal optimization (SMO) algorithm for support vector machine (SVM)regression that is caused by the use...
A Fast Iterative Nearest Point Algorithm for Support Vector Machine Classifier Design (2000)
Keerthi, SS, Shevade, SK, Bhattacharyya, C, Murthy, KRK
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...