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Trading Convexity for Scalability


Book Chapter


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: PDF


  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},
  month_numeric = {9}