Empirical Inference
Conference Paper
1996
Incorporating invariances in support vector learning machines
Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.
| Author(s): | Schölkopf, B. and Burges, C. and Vapnik, V. |
| Book Title: | Artificial Neural Networks: ICANN 96, LNCS vol. 1112 |
| Journal: | Artificial Neural Networks --- ICANN‘96 |
| Pages: | 47-52 |
| Year: | 1996 |
| Month: | July |
| Day: | 0 |
| Editors: | C von der Malsburg and W von Seelen and JC Vorbr{\"u}ggen and B Sendhoff |
| Publisher: | Springer |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Berlin, Germany |
| DOI: | 10.1007/3-540-61510-5_12 |
| Event Name: | 6th International Conference on Artificial Neural Networks |
| Event Place: | Bochum, Germany |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 3-540-61510-5 |
| Note: | volume 1112 of Lecture Notes in Computer Science |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{796,
title = {Incorporating invariances in support vector learning machines},
journal = {Artificial Neural Networks --- ICANN‘96},
booktitle = {Artificial Neural Networks: ICANN 96, LNCS vol. 1112},
abstract = {Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.},
pages = {47-52},
editors = {C von der Malsburg and W von Seelen and JC Vorbr{\"u}ggen and B Sendhoff},
publisher = {Springer},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Berlin, Germany},
month = jul,
year = {1996},
note = {volume 1112 of Lecture Notes in Computer Science
},
slug = {796},
author = {Sch{\"o}lkopf, B. and Burges, C. and Vapnik, V.},
month_numeric = {7}
}