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Convex Neural Networks
Type of publication: Inproceedings
Citation: NIPS2005_583
Booktitle: Advances in Neural Information Processing Systems 18 (NIPS'05)
Year: 2006
Pages: 123--130
Publisher: MIT Press
Address: Cambridge, MA
Crossref: NIPS18:
Advances in Neural Information Processing Systems 18 (NIPS'05), Weiss, Yair, {Schölkopf}, Bernhard and Platt, John (eds), in: Advances in Neural Information Processing Systems 18 (NIPS'05), MIT Press, -1
URL: http://www.iro.umontreal.ca/~l...
Abstract: Convexity has recently received a lot of attention in the machine learning community, and the lack of convexity has been seen as a major disadvantage of many learning algorithms, such as multi-layer artificial neural networks. We show that training multi-layer neural networks in which the number of hidden units is learned can be viewed as a convex optimization problem. This problem involves an infinite number of variables, but can be solved by incrementally inserting a hidden unit at a time, each time finding a linear classifier that minimizes a weighted sum of errors.
Userfields: topics={Boosting},cat={C},
Keywords:
Authors Bengio, Yoshua
Le Roux, Nicolas
Vincent, Pascal
Delalleau, Olivier
Marcotte, Patrice
Editors Weiss, Yair
{Schölkopf}, Bernhard
Platt, John
Added by: [ADM]
Total mark: 0
Attachments
  • convex_nnet_nips2005.pdf
  • http://www.iro.umontreal.ca/~l...
Notes
    Topics