具有潜在变量描述的多实例支持向量机

2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Pub Date : 2013-07-23 DOI:10.1109/FSKD.2013.6816236

J. Lu, Wei Li, Jiabao Wang, Yafei Zhang, Yang Li, Lei Bao

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引用次数: 0

摘要

本文采用隐变量模型对MI-SVM及其特征映射变量进行重新描述。提出了具有潜在变量描述的MI-SVM及其随机优化学习算法。在Musk和Corel数据集中，本文算法的预测精度更高，学习速度更快，对参数和噪声具有较强的稳定性和鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Multiple Instance Support Vector Machines with latent variable description

In this paper, the latent variable model is adopted to re-describe MI-SVM and its feature mapping variants. MI-SVM with latent variable description and the corresponding stochastic optimization learning algorithm are proposed. In the Musk and Corel datasets, the proposed algorithm achieves higher predicting accuracy and faster learning speed, with strong stability and robustness for parameters and noise.

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2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

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