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* | 2008 | |
---|---|---|

55 | EE | 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 |

54 | EE | 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 | ||

53 | EE | Chih-Jen Lin, Ruby C. Weng, S. Sathiya Keerthi: Trust region Newton methods for large-scale logistic regression. ICML 2007: 561-568 |

52 | EE | Vikas Sindhwani, Wei Chu, S. Sathiya Keerthi: Semi-Supervised Gaussian Process Classifiers. IJCAI 2007: 1059-1064 |

51 | EE | S. Sathiya Keerthi, John A. Tomlin: Constructing a maximum utility slate of on-line advertisements CoRR abs/0706.1318: (2007) |

50 | EE | S. Sathiya Keerthi, Shirish Krishnaj Shevade: A Fast Tracking Algorithm for Generalized LARS/LASSO. IEEE Transactions on Neural Networks 18(6): 1826-1830 (2007) |

49 | EE | S. Sundararajan, Shirish Krishnaj Shevade, S. Sathiya Keerthi: Fast Generalized Cross-Validation Algorithm for Sparse Model Learning. Neural Computation 19(1): 283-301 (2007) |

48 | EE | Wei Chu, S. Sathiya Keerthi: Support Vector Ordinal Regression. Neural Computation 19(3): 792-815 (2007) |

2006 | ||

47 | EE | Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chapelle: Deterministic annealing for semi-supervised kernel machines. ICML 2006: 841-848 |

46 | EE | Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keerthi: Branch and Bound for Semi-Supervised Support Vector Machines. NIPS 2006: 217-224 |

45 | EE | Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. Sathiya Keerthi: Relational Learning with Gaussian Processes. NIPS 2006: 289-296 |

44 | EE | S. Sathiya Keerthi, Vikas Sindhwani, Olivier Chapelle: An Efficient Method for Gradient-Based Adaptation of Hyperparameters in SVM Models. NIPS 2006: 673-680 |

43 | EE | Vikas Sindhwani, S. Sathiya Keerthi: Large scale semi-supervised linear SVMs. SIGIR 2006: 477-484 |

42 | EE | 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) |

41 | EE | S. Sathiya Keerthi, Olivier Chapelle, Dennis DeCoste: Building Support Vector Machines with Reduced Classifier Complexity. Journal of Machine Learning Research 7: 1493-1515 (2006) |

40 | EE | 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) |

2005 | ||

39 | EE | Wei Chu, S. Sathiya Keerthi: New approaches to support vector ordinal regression. ICML 2005: 145-152 |

38 | EE | S. Sathiya Keerthi: Generalized LARS as an effective feature selection tool for text classification with SVMs. ICML 2005: 417-424 |

37 | EE | Kaibo Duan, S. Sathiya Keerthi: Which Is the Best Multiclass SVM Method? An Empirical Study. Multiple Classifier Systems 2005: 278-285 |

36 | EE | S. Sathiya Keerthi, Wei Chu: A matching pursuit approach to sparse Gaussian process regression. NIPS 2005 |

35 | EE | 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) |

34 | EE | 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) |

2004 | ||

33 | EE | Shirish Krishnaj Shevade, S. Sundararajan, S. Sathiya Keerthi: Predictive Approaches for Sparse Model Learning. ICONIP 2004: 434-439 |

32 | EE | C. J. 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) |

2003 | ||

31 | EE | 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 |

30 | EE | 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 |

29 | EE | 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 |

28 | Shirish Krishnaj Shevade, S. Sathiya Keerthi: A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinformatics 19(17): 2246-2253 (2003) | |

27 | EE | S. Sathiya Keerthi, Shirish Krishnaj Shevade: SMO Algorithm for Least-Squares SVM Formulation. Neural Computation 15(2): 487-507 (2003) |

26 | EE | S. Sathiya Keerthi, Chih-Jen Lin: Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Computation 15(7): 1667-1689 (2003) |

25 | EE | Wei Chu, S. Sathiya Keerthi, Chong Jin Ong: Bayesian Trigonometric Support Vector Classifier. Neural Computation 15(9): 2227-225 (2003) |

24 | EE | Kaibo Duan, S. Sathiya Keerthi, Aun Neow Poo: Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 51: 41-59 (2003) |

23 | EE | 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) |

2002 | ||

22 | S. Sathiya Keerthi, Kaibo Duan, Shirish Krishnaj Shevade, Aun Neow Poo: A Fast Dual Algorithm for Kernel Logistic Regression. ICML 2002: 299-306 | |

21 | S. Sathiya Keerthi, Elmer G. Gilbert: Convergence of a Generalized SMO Algorithm for SVM Classifier Design. Machine Learning 46(1-3): 351-360 (2002) | |

20 | EE | 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) |

2001 | ||

19 | Wei Chu, S. Sathiya Keerthi, Chong Jin Ong: A Unified Loss Function in Bayesian Framework for Support Vector Regression. ICML 2001: 51-58 | |

18 | EE | Chiranjib Bhattacharyya, S. Sathiya Keerthi: Mean Field Methods for a Special Class of Belief Networks. J. Artif. Intell. Res. (JAIR) 15: 91-114 (2001) |

17 | 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) | |

16 | S. Sundararajan, S. Sathiya Keerthi: Predictive Approaches for Choosing Hyperparameters in Gaussian Processes. Neural Computation 13(5): 1103-1118 (2001) | |

15 | EE | 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) |

2000 | ||

14 | Chiranjib Bhattacharyya, S. Sathiya Keerthi: A Variational Mean-Field Theory for Sigmoidal Belief Networks. NIPS 2000: 374-380 | |

13 | 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) | |

1999 | ||

12 | EE | C. S. Sundaresan, S. Sathiya Keerthi: A Study of Representations for Pen based Handwriting Recognition of Tamil Characters. ICDAR 1999: 422-425 |

11 | EE | K. R. K. Murthy, S. Sathiya Keerthi: Context Filters for Document-based Information Filtering. ICDAR 1999: 709-712 |

10 | S. Sathiya Keerthi, Chong Jin Ong, Eugene Huang, Elmer G. Gilbert: EquiDistance Diagram: A New Roadmap Method for Path Planning. ICRA 1999: 682-687 | |

9 | EE | S. Sundararajan, S. Sathiya Keerthi: Predictive App roaches for Choosing Hyperparameters in Gaussian Processes. NIPS 1999: 631-637 |

1998 | ||

8 | EE | 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 | ||

7 | 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 | |

6 | EE | 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) |

1994 | ||

5 | 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 | ||

4 | 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 | |

3 | Nukala V. R. K. N. Murthy, S. Sathiya Keerthi: Optimal Control of a Somersaulting Platform Diver: A Numerical Approach. ICRA (1) 1993: 1013-1018 | |

2 | K. Sridharan, Harry E. Stephanou, S. Sathiya Keerthi: On Computing a Distance Measure for Path Planning. ICRA (1) 1993: 554-559 | |

1 | Sudhaker Samuel, S. Sathiya Keerthi: Numerical Determination of Optimal Non-Holonomic Paths in the Presence of Obstacles. ICRA (1) 1993: 826-831 |