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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Collobert, R. Bengio, S. |
| Copyright Year | 2004 |
| Description | Author affiliation: IDIAP, Martigny, Switzerland (Collobert, R.; Bengio, S.) |
| Abstract | Several second-order optimization methods for gradient descent algorithms have been proposed over the years, but they usually need to compute the inverse of the Hessian of the cost function (or an approximation of this inverse) during training. In most cases, this leads to an O(n/sup 2/) cost in time and space per iteration, where n is the number of parameters, which is prohibitive for large n. We propose instead a study of the Hessian before training. Based on a second order analysis, we show that a block-diagonal Hessian yields an easier optimization problem than a full Hessian. We also show that the condition of block-diagonality in common machine learning models can be achieved by simply selecting an appropriate training criterion. Finally, we propose a version of the SVM criterion applied to MLPs, which verifies the aspects highlighted in this second order analysis, but also yields very good generalization performance in practice, taking advantage of the margin effect. Several empirical comparisons on two benchmark datasets are given to illustrate this approach. |
| File Size | 293754 |
| File Format | |
| ISBN | 0780384849 |
| ISSN | 15206149 |
| DOI | 10.1109/ICASSP.2004.1327161 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-05-17 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Cost function Support vector machines Optimization methods Performance analysis Multilayer perceptrons Iterative algorithms Stochastic processes Support vector machine classification Approximation algorithms Machine learning |
| Content Type | Text |
| Resource Type | Article |
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