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Convex neural networks
Type of publication: Techreport
Citation: Bengio-convex-05
Number: 1263
Year: 2005
Institution: Département d'informatique et recherche opérationnelle, Université de Montréal
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 how 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 classifiers that minimizes a weighted sum of errors.
Userfields: topics={Boosting},cat={T},
Keywords:
Authors Bengio, Yoshua
Le Roux, Nicolas
Vincent, Pascal
Delalleau, Olivier
Marcotte, Patrice
Added by: [ADM]
Total mark: 0
Attachments
  • TR1263.pdf
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