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 | |
| Added by: | [ADM] |
| Total mark: | 0 |
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