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Seminar on Support Vector Machines 2002

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Topics of interest in the 2002 edition:

- SVM models for Regression
- Kernels: relation to similarity measures; applications; boolean kernels
- Nu-SVM
- Experimental comparison between: NN, SVM, and AdaBoost
- On-line Learning using SVM
- Unsupervised Learning using SVM
- Feature Selection using SVM
- Active Learning and SVM
 

Scheduling:
 
When 
At what time
 What Who Slides Bibliography Where
  29-01
 10:30-11:30h
  Some (open?) Questions About the SVM Paradigm  Enrique Romero
-
 -
Aula C6-103
  12-02
 10:30-11:30h
 Generalization error bounds (I)  José Luis Balcázar
-
 -
Aula C6-002
26-02
 10:30-11:30h Generalization error bounds (II)  José Luis Balcázar
-
-
Aula C6-002
12-03
 10:30-11:30h
  On-line Support Vector Machine Regression  Mario Martín
[1]
[2]
 Biblioteca C6-221
09-04
10:30-11:30h
 An Empirical Comparison Between SVM, NN and AdaBoost  L. Màrquez & E. Romero
-
-
Bblioteca C6-221
23-04
10:30-11:30h
 Nu-SVM  Jorge Castro
-
-
Biblioteca C6-221
14-05
10:30-11:30h
  SVM con interpretación simbólica  Haydemar Núñez
-
-
Biblioteca C6-221
28-05
10:30-11:30h
  Reducing regression to classification (and random sampling)  José Luis Balcázar
-
-
Biblioteca C6-221
26-09
11:00-12:00h
  Kernels on Discrete Structures for Text and Natural Language Processing  L. Màrquez & X. Carreras
-
-
Aula C6-002
05-12 10:30-11:30h Kernels on Structured Domains Lluís Valentín
-
-
Biblioteca C6-221
27-03 12:00-13:00h Una vision global del proyecto TROPPUS José Luis Balcázar
-
 [3]
Biblioteca C6-221

Here you can find a link to the 2001 edition of the seminar on SVMs
 

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Some important links:

- Support Vector Machines in the Web: http://www.kernel-machines.org

- Computational Learning Theory Web page: http://www.learningtheory.org

- Boosting algorithms in the Web: http://www.boosting.org

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Basic bibliography:

- V. Vapnik. The Nature of Statistical Learning Theory. Springer-Verlag, NY, 1995.

- N. Cristianini & J. Shawe-Taylor. An Introduction to Support Vector Machines (and other kernel-based learning methods). http://www.support-vector.net

- A. Smola, P. Bartlett, B. Scholkopf & D. Schuurmans. Advances in Large Margin Classifiers. MIT Press, 2000.

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List of participants
 
Rene Alquézar LSI, UPC
Cecilio Angulo ESAII, UPC
José Luis Balcázar LSI, UPC
Lluís Belanche LSI, UPC
Xavier Carreras LSI, UPC
Jorge Castro LSI, UPC
Jordi Delgado LSI, UPC
Gerard Escudero LSI, UPC
David Guijarro Mannnes Technology
Lluís Màrquez LSI, UPC
Mario Martín LSI, UPC
Àngela Nebot LSI, UPC
Luis Carlos Molina LSI, UPC
Gustavo Pérez FIB, UPC
German Rigau LSI, UPC
Horacio Rodríguez LSI, UPC
Mayra Ruiz Mannnes Technology
Enrique Romero LSI, UPC
Fatos Xhafa LSI, UPC

If you want to be included in the mailing list and in the list of participants, please send an e-mail to:

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Comments to:
Last update: 20/03/2003

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