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Publications | Technical Reports

Found 32 results

Filters: author is Keerthi [Clear All Filters]

2008

Ad Delivery with Budgeted Advertisers: A Comprehensive LP Approach Abrams, Z.; Keerthi, S.S.; Mendelevitch, O.; Tomlin, J.A. , Journal of Electronic Commerce Research, 01/2008, Volume 9, p.16 - 32, (2008) Abstract

2007

An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models Keerthi, S.S.; Sindhwani, V.; Chapelle, O. , Advances in Neural Information Processing Systems 19, Cambridge, MA, p.673--680, (2007)

A Fast Tracking Algorithm for Generalized LARS/LASSO. Keerthi, S.S.; Shevade, S.K. , IEEE Transactions on Neural Networks, Volume 18, Number 4, (2007)

Branch and Bound for Semi-Supervised Support Vector Machines Chapelle, O.; Sindhwani, V.; Keerthi, S.S. , Advances in Neural Information Processing Systems 19, Cambridge, MA, p.217--224, (2007)

Fast Generalized Cross-Validation Algorithm for Sparse Model Learning. Sundararajan, S.; Shevade, S.K.; Keerthi, S.S. , Neural Computation, Volume 19, Number 1, p.283-301, (2007)

Newton Methods for Fast Semisupervised Linear SVMs. Sindhwani, V.; Keerthi, S.S. , Large Scale Kernel Machines, Cambridge, MA, (2007)

Relational Learning with Gaussian Processes Chu, W.; Sindhwani, V.; Ghahramani, Z.; Keerthi, S.S. , Advances in Neural Information Processing Systems 19, Cambridge, MA, p.289--296, (2007)

Support Vector Ordinal Regression. Chu, W.; Keerthi, S.S. , Neural Computation, Volume 19, Number 3, p.792-815, (2007)

Semi-Supervised Gaussian Process Classifiers. Sindhwani, V.; Chu, W.; Keerthi, S.S. , IJCAI, p.1059-1064, (2007)

2006

Building Support Vector Machines with Reduced Classifier Complexity. Keerthi, S.S.; Chapelle, O.; DeCoste, D. , Journal of Machine Learning Research, Volume 7, p.1493-1515, (2006)

Developing parallel sequential minimal optimization for fast training support vector machine. Cao, L.J.; Keerthi, S.S.; Ong, C.J.; Uvaraj, P.; Fu, X.J.; Lee, H.P. , Neurocomputing, Volume 70, Number 1-3, p.93-104, (2006)

Deterministic annealing for semi-supervised kernel machines. Sindhwani, V.; Keerthi, S.S.; Chapelle, O. , ICML, p.841-848, (2006)

Large scale semi-supervised linear SVMs. Sindhwani, V.; Keerthi, S.S. , SIGIR, p.477-484, (2006)

2005

An improved conjugate gradient scheme to the solution of least squares SVM. Chu, W.; Keerthi, S.S.; Ong, C. , IEEE Transactions on Neural Networks, Volume 16, Number 2, p.498-501, (2005)

A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs. Keerthi, S.S.; DeCoste, D. , Journal of Machine Learning Research, Volume 6, p.341-361, (2005)

A matching pursuit approach to sparse Gaussian process regression. Keerthi, S.S.; Chu, W. , NIPS, (2005)

Generalized LARS as an effective feature selection tool for text classification with SVMs. Keerthi, S.S. , ICML, p.417-424, (2005)

New approaches to support vector ordinal regression. Chu, W.; Keerthi, S.S. , ICML, p.145-152, (2005)

Which Is the Best Multiclass SVM Method? An Empirical Study. Duan, K.; Keerthi, S.S. , Multiple Classifier Systems, p.278-285, (2005)

A Fast Dual Algorithm for Kernel Logistic Regression. Keerthi, S.S.; Duan, K.; Shevade, S.; Poo, A. , Machine Learning, Volume 61, Number 1-3, p.151-165, (2005)

2004

An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels. Lee, M.M.S.; Keerthi, S.S.; Ong, C.; DeCoste, D. , IEEE Transactions on Neural Networks, Volume 15, Number 3, p.750-757, (2004)

Bayesian support vector regression using a unified loss function. Chu, W.; Keerthi, S.S.; Ong, C. , IEEE Transactions on Neural Networks, Volume 15, Number 1, p.29-44, (2004)

Predictive Approaches for Sparse Model Learning. Shevade, S.K.; Sundararajan, S.; Keerthi, S.S. , ICONIP, p.434-439, (2004)

Stability regions for constrained nonlinear systems and their functional characterization via support-vector-machine learning. Ong, C.J.; Gilbert, E.G.; Zhang, Z.H.; Keerthi, S.S. , Automatica, (2004)

2003

A simple and efficient algorithm for gene selection using sparse logistic regression. Shevade, S.K.; Keerthi, S.S. , Bioinformatics, Volume 19, Number 17, p.2246-2253, (2003)