给出了一种具有部分经验风险的支持向量分类模型

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2015-12-01 DOI:10.1109/ICMLA.2015.45

Linkai Luo, Ling-Jun Ye, Qifeng Zhou, Hong Peng

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引用次数: 0

摘要

提出了一种具有部分经验风险的支持向量分类模型(P-SVC)。给出了P-SVC的顺序最小优化方法。P-SVC是经典支持向量分类(C-SVC)的扩展，可用于要求部分经验风险的情况。在一些人工数据集和基准数据集上的实验表明，当部分经验风险已知时，P-SVC比C-SVC获得了更好的分类精度和更稳定的分类结果。

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A Support Vector Classification Model with Partial Empirical Risks Given

A novel model of support vector classification with partial empirical risks given (P-SVC) is proposed. A sequential minimal optimization for P-SVC is also provided. P-SVC is an extension of the classical support vector classification (C-SVC) and can be used in the case where partial empirical risks are requested. The experiments on some artificial and benchmark datasets show P-SVC obtains a better classification accuracy and a more stable classification result than C-SVC does when partial empirical risks are known.

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2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)

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