| 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 |
