Breaking SVM Complexity with Cross-Training

Goekhan Bakir, Leon Bottou and Jason Weston

NIPS 2004


Abstract

We propose to selectively remove examples from the training set using probabilistic estimates related to editing algorithms. This heuristic procedure aims at creating a separable distribution of training examples with minimal impact on the position of the decision boundary. It breaks the linear dependency between the number of SVs and the number of training examples, and sharply reduces the complexity of SVMs during both the training and prediction stages.

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