多类支持向量机的一种高效类smo算法

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI:10.1109/NNSP.2002.1030041

F. Aiolli, A. Sperduti

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引用次数: 14

摘要

从Cramer和Singer(参见Journal of Machine Learning Research, vol.2, p.265-92, Dec. 2001)多类核机的重新公式出发，我们提出了一种类似顺序最小优化(SMO)的拉格朗日量增量和快速优化算法。提出的公式使我们能够定义非常有效的新模式选择策略，从而获得更好的实证结果。

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An efficient SMO-like algorithm for multiclass SVM

Starting from a reformulation of Cramer and Singer (see Journal of Machine Learning Research, vol.2, p.265-92, Dec. 2001) multiclass kernel machine, we propose a sequential minimal optimization (SMO) like algorithm for incremental and fast optimization of the Lagrangian. The proposed formulation allowed us to define very effective new pattern selection strategies which lead to better empirical results.

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Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing

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