Reshma Khemchandani

Twin Support Vector Machines for Pattern Classification

We propose twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data

TWSVR: Regression via Twin Support Vector Machine.

Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets.

Twin support vector regression for the simultaneous learning of a function and its derivatives

Twin support vector regression (TSVR) determines a pair of

Color image classification and retrieval through ternary decision structure based multi-category TWSVM

\epsilon

Generalized eigenvalue proximal support vector regressor

-insensitive up- and down-bound functions by solving two related support vector machine-type problems, each of which is smaller than that in a classical SVR. On the lines of TSVR, we have proposed a novel regressor for the simultaneous learning of a function and its derivatives, termed as TSVR of a Function and its Derivatives. Results over several functions of more than one variable demonstrate its effectiveness over other existing approaches in terms of improving the estimation accuracy and reducing run time complexity.

Fuzzy least squares twin support vector clustering

To utilize the structural information present in multidimensional features of an object, a tensor-based learning framework, termed as support tensor machines (STMs), was developed on the lines of support vector machines. In order to improve it further we have developed a least squares variant of STM, termed as proximal support tensor machine (PSTM), where the classifier is obtained by solving a system of linear equations rather than a quadratic programming problem at each iteration of PSTM algorithm as compared to STM algorithm. This in turn provides a significant reduction in the computation time, as well as comparable classification accuracy. The efficacy of the proposed method has been demonstrated in simulations over face detection and handwriting recognition datasets.

Multi-category laplacian least squares twin support vector machine

In this paper, we propose Ternary Decision Structure based multi-category twin support vector machines (TDS-TWSVM) classifier. Twin support vector machines (TWSVM) formulation deals with finding non-parallel plane classifier which is obtained by solving two related Quadratic Programming Problems (QPPs). The proposed TDS-TWSVM classifier is an extension of TWSVM so as to handle multi-class data and is more efficient in terms of training and testing time of classifiers. For a K-class problem, a balanced ternary structure requires Â? log 3 K Â? comparisons for evaluating a test sample. The experimental results depict that TDS-TWSVM outperforms One-Against-All TWSVM (OAA-TWSVM) and binary tree-based TWSVM (TB-TWSVM) in terms of classification accuracy. We have shown the efficacy of the proposed algorithm via image classification and further for image retrieval. Experiments are performed on a varied range of benchmark image databases with 5-fold cross validation.

Regularized Least Squares Fuzzy Support Vector Regression for Time Series Forecasting

Graphical abstractDisplay Omitted HighlightsIntroduces hierarchical approach to deal with multi-class activity classification problem.LS-TWSVM based classifier that deals with noise in activity recognition framework.Introduce the incremental version of RLS-TWSVM in activity recognition framework. Human activity recognition is an active area of research in Computer Vision. One of the challenges of activity recognition system is the presence of noise between related activity classes along with high training and testing time complexity of the system. In this paper, we address these problems by introducing a Robust Least Squares Twin Support Vector Machine (RLS-TWSVM) algorithm. RLS-TWSVM handles the heteroscedastic noise and outliers present in activity recognition framework. Incremental RLS-TWSVM is proposed to speed up the training phase. Further, we introduce the hierarchical approach with RLS-TWSVM to deal with multi-category activity recognition problem. Computational comparisons of our proposed approach on four well-known activity recognition datasets along with real world machine learning benchmark datasets have been carried out. Experimental results show that our method is not only fast but, yields significantly better generalization performance and is robust in order to handle heteroscedastic noise and outliers.