基于多视图双支持向量机半监督学习机
Semi-Supervised Learning Machine Based on Multi-View Twin Support Vector Machine
DOI: 10.12677/ORF.2019.92021, PDF,   
作者: 姚瑞：新疆大学，数学与系统科学学院，新疆 乌鲁木齐
关键词: 半监督学习；分类问题；支持向量机；多视图；Semi-Supervised Learning； Classification； Support Vector Machines； Multi-View
摘要: 在机器学习的诸多实际问题中，数据有多个视图，多个视图优势互补，分类效果更好。本文主要针对多视图双支持向量机的半监督方法进行了研究，根据不同的特征将数据分成多个视图，分别给每个视图找两个非平行超平面，构建模型，求解对偶问题。实验结果表明，本文的算法可以对数据进行降维，有很好的分类精确度，该算法较双支持向量机算法缩短了运行时间，减少了计算复杂度，预测性能较好。
Abstract: In many practical problems of machine learning, the data has multi-views; multi-views complement each other; and the classification effect is better. This paper mainly studies the semi-supervised method of multi-view dual support vector machine, and divides the data according to different characteristics. Multi-views are used to find two non-parallel hyperplanes for each view, and the model is constructed to solve the dual problem. Experimental results show that the proposed algorithm can reduce the dimension of the data and has good classification accuracy. The support vector machine algorithm shortens the running time, reduces the computational complexity, and predicts better performance.
文章引用：姚瑞. 基于多视图双支持向量机半监督学习机[J]. 运筹与模糊学, 2019, 9(2): 177-188. https://doi.org/10.12677/ORF.2019.92021

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