Sathiya Keerthi Selvaraj
List of publications from the DBLP Bibliography Server - FAQ
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 | 2012 | |
|---|---|---|
| j38 |      | Paramveer S. Dhillon, S. Sathiya Keerthi, Kedar Bellare, Olivier Chapelle, Sundararajan Sellamanickam: Deterministic Annealing for Semi-Supervised Structured Output Learning. Journal of Machine Learning Research - Proceedings Track 22: 299-307 (2012) |
| c35 | | Sathiya Keerthi Selvaraj, Sundararajan Sellamanickam, Shirish Krishnaj Shevade: Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses. COLING (Posters) 2012: 1091-1100 |
| c34 | | Sundararajan Sellamanickam, Charu Tiwari, Sathiya Keerthi Selvaraj: Regularized Structured Output Learning with Partial Labels. SDM 2012: 1059-1070 |
| i6 | | Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj: Graph Based Classification Methods Using Inaccurate External Classifier Information. CoRR abs/1206.5915 (2012) |
| i5 | | Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj: Transductive Classification Methods for Mixed Graphs. CoRR abs/1206.6015 (2012) |
| i4 | | Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj: Predictive Approaches For Gaussian Process Classifier Model Selection. CoRR abs/1206.6038 (2012) |
| i3 | | Sathiya Keerthi Selvaraj, Sundararajan Sellamanickam, Shirish Krishnaj Shevade: Extension of TSVM to Multi-Class and Hierarchical Text Classification Problems With General Losses. CoRR abs/1211.0210 (2012) |
| 2011 | ||
| c33 | | Sathiya Keerthi Selvaraj, Bigyan Bhar, Sundararajan Sellamanickam, Shirish Krishnaj Shevade: Semi-supervised SVMs for classification with unknown class proportions and a small labeled dataset. CIKM 2011: 653-662 |
| c32 | | Sundararajan Sellamanickam, Priyanka Garg, Sathiya Keerthi Selvaraj: A pairwise ranking based approach to learning with positive and unlabeled examples. CIKM 2011: 663-672 |
| c31 | | Paramveer S. Dhillon, Sundararajan Sellamanickam, Sathiya Keerthi Selvaraj: Semi-supervised multi-task learning of structured prediction models for web information extraction. CIKM 2011: 957-966 |
| c30 | | Shirish Krishnaj Shevade, Balamurugan P., S. Sundararajan, S. Sathiya Keerthi: A Sequential Dual Method for Structural SVMs. SDM 2011: 223-234 |
| i2 | | Chiru Bhattacharyya, S. Sathiya Keerthi: Mean Field Methods for a Special Class of Belief Networks. CoRR abs/1106.0246 (2011) |
| 2010 | ||
| j37 | | Olivier Chapelle, S. Sathiya Keerthi: Efficient algorithms for ranking with SVMs. Inf. Retr. 13(3): 201-215 (2010) |
| 2008 | ||
| j36 | | Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi: Optimization Techniques for Semi-Supervised Support Vector Machines. Journal of Machine Learning Research 9: 203-233 (2008) |
| j35 | | Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi: Trust Region Newton Method for Logistic Regression. Journal of Machine Learning Research 9: 627-650 (2008) |
| c29 | | Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin, S. Sathiya Keerthi, S. Sundararajan: A dual coordinate descent method for large-scale linear SVM. ICML 2008: 408-415 |
| c28 | | S. Sathiya Keerthi, S. Sundararajan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin: A sequential dual method for large scale multi-class linear svms. KDD 2008: 408-416 |
| 2007 | ||
| j34 | | S. Sundararajan, Shirish Krishnaj Shevade, S. Sathiya Keerthi: Fast Generalized Cross-Validation Algorithm for Sparse Model Learning. Neural Computation 19(1): 283-301 (2007) |
| j33 | | Wei Chu, S. Sathiya Keerthi: Support Vector Ordinal Regression. Neural Computation 19(3): 792-815 (2007) |
| j32 | | S. Sathiya Keerthi, Shirish Krishnaj Shevade: A Fast Tracking Algorithm for Generalized LARS/LASSO. IEEE Transactions on Neural Networks 18(6): 1826-1830 (2007) |
| c27 | | Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi: Trust region Newton methods for large-scale logistic regression. ICML 2007: 561-568 |
| c26 | | Vikas Sindhwani, Wei Chu, S. Sathiya Keerthi: Semi-Supervised Gaussian Process Classifiers. IJCAI 2007: 1059-1064 |
| i1 | | S. Sathiya Keerthi, John A. Tomlin: Constructing a maximum utility slate of on-line advertisements. CoRR abs/0706.1318 (2007) |
| 2006 | ||
| j31 | | L. J. Cao, S. Sathiya Keerthi, Chong Jin Ong, P. Uvaraj, Xiu Ju Fu, H. P. Lee: Developing parallel sequential minimal optimization for fast training support vector machine. Neurocomputing 70(1-3): 93-104 (2006) |
| j30 | | S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste: Building Support Vector Machines with Reduced Classifier Complexity. Journal of Machine Learning Research 7: 1493-1515 (2006) |
| j29 | | L. J. Cao, S. Sathiya Keerthi, Chong Jin Ong, J. Q. Zhang, U. Periyathamby, Xiu Ju Fu, H. P. Lee: Parallel sequential minimal optimization for the training of support vector machines. IEEE Transactions on Neural Networks 17(4): 1039-1049 (2006) |
| c25 | | Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle: Deterministic annealing for semi-supervised kernel machines. ICML 2006: 841-848 |
| c24 | | Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi: Branch and Bound for Semi-Supervised Support Vector Machines. NIPS 2006: 217-224 |
| c23 | | Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi: Relational Learning with Gaussian Processes. NIPS 2006: 289-296 |
| c22 | | S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle: An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models. NIPS 2006: 673-680 |
| c21 | | |
| 2005 | ||
| j28 | | S. Sathiya Keerthi, Dennis DeCoste: A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs. Journal of Machine Learning Research 6: 341-361 (2005) |
| j27 | | S. Sathiya Keerthi, Kaibo Duan, Shirish Krishnaj Shevade, A. Poo: A Fast Dual Algorithm for Kernel Logistic Regression. Machine Learning 61(1-3): 151-165 (2005) |
| j26 | | Rakesh Menon, Han Tong Loh, S. Sathiya Keerthi: Analyzing textual databases using data mining to enable fast product development processes. Rel. Eng. & Sys. Safety 88(2): 171-180 (2005) |
| j25 | | Wei Chu, Chong Jin Ong, S. Sathiya Keerthi: An improved conjugate gradient scheme to the solution of least squares SVM. IEEE Transactions on Neural Networks 16(2): 498-501 (2005) |
| c20 | | |
| c19 | | S. Sathiya Keerthi: Generalized LARS as an effective feature selection tool for text classification with SVMs. ICML 2005: 417-424 |
| c18 | | Kaibo Duan, S. Sathiya Keerthi: Which Is the Best Multiclass SVM Method? An Empirical Study. Multiple Classifier Systems 2005: 278-285 |
| c17 | | |
| 2004 | ||
| j24 | | Chong Jin Ong, S. Sathiya Keerthi, Elmer G. Gilbert, Z. H. Zhang: Stability regions for constrained nonlinear systems and their functional characterization via support-vector-machine learning. Automatica 40(11): 1955-1964 (2004) |
| j23 | | Wei Chu, S. Sathiya Keerthi, Chong Jin Ong: Bayesian support vector regression using a unified loss function. IEEE Transactions on Neural Networks 15(1): 29-44 (2004) |
| j22 | | Martin M. S. Lee, S. Sathiya Keerthi, Chong Jin Ong, Dennis DeCoste: An efficient method for computing leave-one-out error in support vector machines with Gaussian kernels. IEEE Transactions on Neural Networks 15(3): 750-757 (2004) |
| c16 | | Shirish Krishnaj Shevade, S. Sundararajan, S. Sathiya Keerthi: Predictive Approaches for Sparse Model Learning. ICONIP 2004: 434-439 |
| 2003 | ||
| j21 | | Shirish Krishnaj Shevade, S. Sathiya Keerthi: A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinformatics 19(17): 2246-2253 (2003) |
| j20 | | Kaibo Duan, S. Sathiya Keerthi, Aun Neow Poo: Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 51: 41-59 (2003) |
| j19 | | Colin Campbell, Chih-Jen Lin, S. Sathiya Keerthi, V. David Sánchez A.: Special issue on support vector machines. Neurocomputing 55(1-2): 1-3 (2003) |
| j18 | | S. Sathiya Keerthi, Shirish Krishnaj Shevade: SMO Algorithm for Least-Squares SVM Formulation. Neural Computation 15(2): 487-507 (2003) |
| j17 | | S. Sathiya Keerthi, Chih-Jen Lin: Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Computation 15(7): 1667-1689 (2003) |
| j16 | | Wei Chu, S. Sathiya Keerthi, Chong Jin Ong: Bayesian Trigonometric Support Vector Classifier. Neural Computation 15(9): 2227-225 (2003) |
| c15 | | Min Shi, David S. Edwin, Rakesh Menon, Lixiang Shen, Jonathan Y. K. Lim, Han Tong Loh, S. Sathiya Keerthi, Chong Jin Ong: A Machine Learning Approach for the Curation of Biomedical Literature. ECIR 2003: 597-604 |
| c14 | | Rakesh Menon, Han Tong Loh, S. Sathiya Keerthi, Aarnout Brombacher: Automated Text Classification for Fast Feedback - Investigating the Effects of Document Representation. KES 2003: 1008-1014 |
| c13 | | Kaibo Duan, S. Sathiya Keerthi, Wei Chu, Shirish Krishnaj Shevade, Aun Neow Poo: Multi-category Classification by Soft-Max Combination of Binary Classifiers. Multiple Classifier Systems 2003: 125-134 |
| 2002 | ||
| j15 | | S. Sathiya Keerthi, Elmer G. Gilbert: Convergence of a Generalized SMO Algorithm for SVM Classifier Design. Machine Learning 46(1-3): 351-360 (2002) |
| j14 | | S. Sathiya Keerthi, Chong Jin Ong, Keng Boon Siah, David B. L. Lim, Wei Chu, Min Shi, David S. Edwin, Rakesh Menon, Lixiang Shen, Jonathan Y. K. Lim, Han Tong Loh: A Machine Learning Approach for the Curation of Biomedical Literature - KDD Cup 2002 (Task 1). SIGKDD Explorations 4(2): 93-94 (2002) |
| j13 | | S. Sathiya Keerthi: Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms. IEEE Transactions on Neural Networks 13(5): 1225-1229 (2002) |
| c12 |  | S. Sathiya Keerthi, Kaibo Duan, Shirish Krishnaj Shevade, Aun Neow Poo: A Fast Dual Algorithm for Kernel Logistic Regression. ICML 2002: 299-306 |
| 2001 | ||
| j12 | | Chiranjib Bhattacharyya, S. Sathiya Keerthi: Mean Field Methods for a Special Class of Belief Networks. J. Artif. Intell. Res. (JAIR) 15: 91-114 (2001) |
| j11 | | K. Sridharan, S. Sathiya Keerthi: Computation of a penetration measure between 3D convex polyhedral objects for collision detection. J. Field Robotics 18(11): 623-631 (2001) |
| j10 | | S. Sathiya Keerthi, Shirish Krishnaj Shevade, Chiranjib Bhattacharyya, K. R. K. Murthy: Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation 13(3): 637-649 (2001) |
| j9 | | S. Sundararajan, S. Sathiya Keerthi: Predictive Approaches for Choosing Hyperparameters in Gaussian Processes. Neural Computation 13(5): 1103-1118 (2001) |
| j8 | | K. R. K. Murthy, S. Sathiya Keerthi, M. Narasimha Murty: Rule prepending and post-pruning approach to incremental learning of decision lists. Pattern Recognition 34(8): 1697-1699 (2001) |
| c11 | | Wei Chu, S. Sathiya Keerthi, Chong Jin Ong: A Unified Loss Function in Bayesian Framework for Support Vector Regression. ICML 2001: 51-58 |
| 2000 | ||
| j7 | | S. Sathiya Keerthi, Shirish Krishnaj Shevade, Chiranjib Bhattacharyya, K. R. K. Murthy: A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Trans. Neural Netw. Learning Syst. 11(1): 124-136 (2000) |
| j6 | | Shirish Krishnaj Shevade, S. Sathiya Keerthi, Chiranjib Bhattacharyya, K. R. K. Murthy: Improvements to the SMO algorithm for SVM regression. IEEE Trans. Neural Netw. Learning Syst. 11(5): 1188-1193 (2000) |
| j5 | | G. Phanendra Babu, M. Narasimha Murty, S. Sathiya Keerthi: A stochastic connectionist approach for global optimization with application to pattern clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B 30(1): 10-24 (2000) |
| c10 | | Chiranjib Bhattacharyya, S. Sathiya Keerthi: A Variational Mean-Field Theory for Sigmoidal Belief Networks. NIPS 2000: 374-380 |
| 1999 | ||
| c9 | | C. S. Sundaresan, S. Sathiya Keerthi: A Study of Representations for Pen based Handwriting Recognition of Tamil Characters. ICDAR 1999: 422-425 |
| c8 | | K. R. K. Murthy, S. Sathiya Keerthi: Context Filters for Document-based Information Filtering. ICDAR 1999: 709-712 |
| c7 | | S. Sathiya Keerthi, Chong Jin Ong, Eugene Huang, Elmer G. Gilbert: EquiDistance Diagram: A New Roadmap Method for Path Planning. ICRA 1999: 682-687 |
| c6 | | S. Sundararajan, S. Sathiya Keerthi: Predictive App roaches for Choosing Hyperparameters in Gaussian Processes. NIPS 1999: 631-637 |
| 1998 | ||
| j4 | | Dipti Deodhare, M. Vidyasagar, S. Sathiya Keerthi: Synthesis of fault-tolerant feedforward neural networks using minimax optimization. IEEE Transactions on Neural Networks 9(5): 891-900 (1998) |
| 1995 | ||
| j3 | | Abhi Dattasharma, S. Sathiya Keerthi: An Augmented Voronoi Roadmap for 3D Translational Motion Planning for a Convex Polyhedron Moving Amidst Convex Polyhedral Obstacles. Theor. Comput. Sci. 140(2): 205-230 (1995) |
| c5 | | Vijay Chandru, Abhi Dattasharma, S. Sathiya Keerthi, N. K. Sancheti, V. Vinay: Algorithms for the Optimal Loading of Recursive Neural Nets. SODA 1995: 342-349 |
| 1994 | ||
| j2 | | K. Sridharan, Harry E. Stephanou, K. C. Craig, S. Sathiya Keerthi: Distance Measures on Intersecting Objects and Their Applications. Inf. Process. Lett. 51(4): 181-188 (1994) |
| 1993 | ||
| c4 | | Abhi Dattasharma, S. Sathiya Keerthi: Translational Motion Planning for a Convex Polyhedron in a 3D Polyhedral World Using an Efficient and New Roadmap. CCCG 1993: 449-454 |
| c3 | | K. Sridharan, Harry E. Stephanou, S. Sathiya Keerthi: On Computing a Distance Measure for Path Planning. ICRA (1) 1993: 554-559 |
| c2 | | Sudhaker Samuel, S. Sathiya Keerthi: Numerical Determination of Optimal Non-Holonomic Paths in the Presence of Obstacles. ICRA (1) 1993: 826-831 |
| c1 | | Nukala V. R. K. N. Murthy, S. Sathiya Keerthi: Optimal Control of a Somersaulting Platform Diver: A Numerical Approach. ICRA (1) 1993: 1013-1018 |
| 1992 | ||
| j1 | | Makarand S. Phatak, S. Sathiya Keerthi: A homotopy approach for stabilizing single-input systems with control structure constraints. Automatica 28(5): 981-987 (1992) |
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