Trading Convexity for Scalability
2007
Book Chapter
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 nonconvexity 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: | Large Scale Kernel Machines |
Pages: | 275-300 |
Year: | 2007 |
Month: | September |
Day: | 0 |
Series: | Neural Information Processing |
Editors: | Bottou, L. , O. Chapelle, D. DeCoste, J. Weston |
Publisher: | MIT Press |
Department(s): | Empirical Inference |
Bibtex Type: | Book Chapter (inbook) |
Address: | Cambridge, MA, USA |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @inbook{4435, title = {Trading Convexity for Scalability}, author = {Collobert, R. and Sinz, F. and Weston, J. and Bottou, L.}, booktitle = {Large Scale Kernel Machines}, pages = {275-300}, series = {Neural Information Processing}, editors = {Bottou, L. , O. Chapelle, D. DeCoste, J. Weston}, publisher = {MIT Press}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Cambridge, MA, USA}, month = sep, year = {2007}, doi = {}, month_numeric = {9} } |