DMML Program 2003-04
The Support Vector
Machines Group
|
DMML Program 2003-04 The Support Vector Machines Group |
| Note to Contributors of resources |
To submit your references, please email epf@samsi.info with the journal citation in the following format: Notes:
|
| Software | Datasets | Web links |
| References | Topics Covered |
| Marc G. Genton; 2001, Classes of Kernels for Machine Learning: A Statistics Perspective, JMLR, 2(Dec), 299-312, | Kernels |
| Andreas Christmann, Ingo Steinwart, (200x), On Robustness Properties of Convex Risk Minimization Methods for Pattern
Recognition. (eingereicht) |
Robustness |
| Ingo Steinwart, 2001, On the Influence of the Kernel on the Consistency of Support Vector Machines, JMLR, 2(Nov), 67-93, 2001. | Kernels |
| Szummer, M. and Jaakola, T. (200x). Information Regularization with Partially Labelled Data | Classification, Unlabelled data |
| Roweis, S, (200x). Locally Linear Embedding | Embedding (Link provided by Jeongyoun Ahn) |
| Szummer, M. and Jaakola, T. (200x), Partially Labelled Data with Markov Random Walks | Classication, Unlabelled data |
| Matthew W.B. Trotter, Bernard F. Buxton & Sean B. Holden. (2001) Support Vector Machines in Combinatorial Chemistry, Submitted to special edition of Measurement & Control, Sep 2001. | Statistical Analysis of High Throughput Data (Link provided by Taiyeong Lee) |
| Szummer, M et al. (200x), Learning from Partially Observed Data | Classification |
| Yu, H et al., (200x). Classifying Large Data Set Using SVM with Hierarchical Clusters. ACM | Classification, Clustering, Support Vector Machines (Link provided by Erik Andries) |
| Bishop and Tipping (2000). Variational Relevance Vector Machines. In Proceedings of the 16th Conference in Uncertainty and Artificial Intelligence (Eds: C Boutilier and M Goldszmidt). |
Bayesian treatment of Support Vector Machines |
| FIGUEIREDO, M (2002) ADAPTIVE SPARSENESS USING JEFFREY'S PRIOR. IN
NEURAL INFORMATION PROCESSING SYSTEMS-NIPS 14 (EDS: T DIETTEERICH, S
BECKER , Z GHAHRAMANI), PP 697-704, CAMBRIDGE, MA:MIT PRESS. |
|
| TIPPING M (2000) THE RELEVANCE VECTOR MACHINE. IN NEURAL INFORMATION PROCESSING SYSTEMS-NIPS 12 (EDS: S SOLLA, T LEEN , K MULLER), PP 652-658, CAMBRIDGE, MA:MIT PRESS. | |
| SOLLICH, P (2001) BAYESIAN METHODS FOR SUPPORT VECTOR MACHINE: EVIDENCE AND PREDICTIVE CLASS PROBABILITIES. MACHINE LEARNING , 46, 21-52. | |
| LAW AND KWOK , (2000) BAYESIAN SUPPORT VECTOR REGESSION. | |
| MALLICK B, GHOSH D, GHOSH M (TO APPEAR IN 2003), BAYESIAN CLASSIFICATION OF TUMORS USING GENE EXPRESSION DATA, JRSS B. |
|
| Johan Suykens, Benchmarking Least Squares Support Vector Machine Classifiers.Datasets for benchmark here or here | |
1. Weston, J., S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio and V. |
Feature Selection. References contributed by Kyupil Yeon, Seoul National University |
| SchOlkopf, B., Smola, A. J., Mtiller, K.-R., Burges, C. J. C., and Vapnik, V. (1998). Support vector methods in learning and feature extraction. In Down, T., Frean, M., and Gallagher, M., editors, Proceedings of the Ninth Australian Congress on Neural Networks, Brisbane, Australia. University of Queensland. |
Feature Selection. References contributed by Kyupil Yeon, Seoul National University |
| Guyon, I., and Elisseeff, A. (2003). An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3,1157- 1182. |
Feature Selection. References contributed by Kyupil Yeon, Seoul National University |
| Bengio, S. (2003), An Introduction to Statistical Machine Learning - Feature Selection, lecture note. |
Feature Selection. References contributed by Kyupil Yeon, Seoul National University |
| Brank, J., Grobelnik, M., Milic-Frayling, N. 2 and Mladenic, D. (2002?) Feature Selection Using Support Vector Machines. |
Feature Selection. References contributed by Kyupil Yeon, Seoul National University |
| C.-W. Hsu and C.-J. Lin. A comparison on methods for multi-class support vector machines , IEEE Transactions on Neural Networks, 13(2002), 415-425. Software and details |
Multicategory SVM. References contributed by Peng Liu. |
| Name | Description | Reference | Comments |
| The Drat Package | Banks et al. | Banks, D et al. | Provide by David Banks |
| BatchAdjust | BatchAdjust.readme | NA | NA |
| Name | Description | References | Source |
| Link | Topic | Rating/Comments |
| Locally Linear Embedding | Local Linear Embedding | Jeongyoun Ahn suggests an examination of this paper for a statistical understanding of its methodology, and possibly some extensions thereof. |
| Kernel LLE | Embedding | NA |
| Feature Selection | Feature Selection | NA |
| Support Vector Machines Tutorials | Support Vector Machines | Good site with a variety of useful introductory material |
| Snowbird (Utah) Summer Workshop | Machine Learning, Statistics and Discovery | Interesting |
| Name | Affiliation | Interests |
| Prem Goel | The Ohio State University | |
| Marc Genton | North Carolina State University | |
| Ernest Fokoue | SAMSI | |
| Helen Zhang | North Carolina State University | |
| Lin Xiaodong | SAMSI/NISS | |
| Peng Liu | North Carolina State University | |
| Jeongyoun Ahn | University of North Carolina/Chapel Hill | |
| Sounak Chakrabotty | University of Florida | |
| Taiyeong Lee | SAS | |
| Maly Ghosh | Univeristy of Florida | I have been currently involved in several RVM and SVM problems involving ``Large p Small n''. These are (1) Binary Classification; (2) Univariate and Multivariate Regression; (3) Multicategory Regression; (4) Multicategory Classification. My approach is fully Bayesian. It involves a likelihood and a prior. I am collaborating with Bani Mallick, Debashis Ghosh and Sounak Chakraborty. Sounak is my Ph.D. student, and was present in last week's meeting. |
| Erik Andries | University of New Mexico | |
| Atina Brooks | North Carolina State University | |
| Stan Young | NISS |
| © 2002-2004, Statistical and Applied Mathematical Sciences Institute. All rights reserved. | Please your comments about this site to epf |