Trading Convexity for Scalability
2006
Conference Paper
ei
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how non-convexity can provide scalability advantages over convexity. We show how concave-convex programming can be applied to produce (i) faster SVMs where training errors are no longer support vectors, and (ii) much faster Transductive SVMs.
Author(s): | Collobert, R. and Sinz, F. and Weston, J. and Bottou, L. |
Book Title: | ICML 2006 |
Journal: | Proceedings of the 23rd International Conference on Machine Learning (ICML 2006) |
Pages: | 201-208 |
Year: | 2006 |
Month: | June |
Day: | 0 |
Editors: | Cohen, W. W., A. Moore |
Publisher: | ACM Press |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1145/1143844.1143870 |
Event Name: | 23rd International Conference on Machine Learning |
Event Place: | Pittsburgh, PA, USA |
Address: | New York, NY, USA |
Digital: | 0 |
Institution: | Association for Computing Machinery |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inproceedings{3917, title = {Trading Convexity for Scalability}, author = {Collobert, R. and Sinz, F. and Weston, J. and Bottou, L.}, journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)}, booktitle = {ICML 2006}, pages = {201-208}, editors = {Cohen, W. W., A. Moore}, publisher = {ACM Press}, organization = {Max-Planck-Gesellschaft}, institution = {Association for Computing Machinery}, school = {Biologische Kybernetik}, address = {New York, NY, USA}, month = jun, year = {2006}, doi = {10.1145/1143844.1143870}, month_numeric = {6} } |