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papers:collobert-weston-bottou-2006 [2006/04/18 17:59] leonb |
papers:collobert-weston-bottou-2006 [2007/01/31 14:45] leonb |
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| - | ===== Trading Convexity for Scalability ===== | ||
| - | // | ||
| - | 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. | ||
| - | we show how non-convexity can provide scalability advantages over | ||
| - | convexity. | ||
| - | (i) faster SVMs where training errors are no longer support vectors, and | ||
| - | (ii) much faster Transductive SVMs. | ||
| - | |||
| - | <box 99% orange> | ||
| - | Ronan Collobert, Jason Weston and Léon Bottou: Trading Convexity for Scalability, | ||
| - | |||
| - | [[http:// | ||
| - | [[http:// | ||
| - | [[http:// | ||
| - | </ | ||
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| - | @inproceedings{collobert-weston-bottou-2006, | ||
| - | author = {Collobert, Ronan and Weston, Jason and Bottou, L\' | ||
| - | title = {Trading Convexity for Scalability}, | ||
| - | year = {2006}, | ||
| - | booktitle = {Proceedings of the Twenty-third International Conference on Machine Learning (ICML 2006)}, | ||
| - | publisher = {IMLS/ | ||
| - | note = {ACM Digital Library}, | ||
| - | url = {http:// | ||
| - | } | ||
| - | |||
| - | ==== 1. Source Code ==== | ||
| - | |||
| - | To be found here soon. | ||