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| ===== Breaking SVM Complexity with Cross-Training ===== | ===== Breaking SVM Complexity with Cross-Training ===== | ||
| + | We propose to selectively remove examples from the training set using | ||
| + | probabilistic estimates related to editing algorithms | ||
| + | (Devijver and Kittler, 1982). | ||
| + | separable distribution of training examples with minimal impact on the | ||
| + | position of the decision boundary. | ||
| + | the number of SVs and the number of training examples, and sharply reduces the | ||
| + | complexity of SVMs during both the training and prediction stages. | ||
| <box 99% orange> | <box 99% orange> | ||
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| + | ==== Notes ==== | ||
| + | |||
| + | Better solutions for this problem are discussed in | ||
| + | [[bordes-ertekin-weston-bottou-2005|(Bordes et al., 2006)]] | ||
| + | and [[collobert-weston-bottou-2006|(Collobert et al., 2006)]]. | ||