论文标题
部分可观测时空混沌系统的无模型预测
MLPSVM:A new parallel support vector machine to multi-label learning
论文作者
论文摘要
多标签学习吸引了机器学习社区的注意力。问题转换方法二进制相关性将熟悉的单个标签转换为多标签算法。由于其简单的结构和有效的算法，二进制相关方法被广泛使用。但是二进制相关性并不考虑标签之间的链接，因此处理某些任务很麻烦。本文提出了一种多标签学习算法，也可以用于单标签分类。它基于标准支持向量机，并将原始的单个决策超平面更改为两个并行决策超平面，它们调用多标签并行支持向量机（MLPSVM）。在本文的末尾，将MLPSVM与其他多标签学习算法进行了比较。实验结果表明，该算法在数据集上表现良好。
Multi-label learning has attracted the attention of the machine learning community. The problem conversion method Binary Relevance converts a familiar single label into a multi-label algorithm. The binary relevance method is widely used because of its simple structure and efficient algorithm. But binary relevance does not consider the links between labels, making it cumbersome to handle some tasks. This paper proposes a multi-label learning algorithm that can also be used for single-label classification. It is based on standard support vector machines and changes the original single decision hyperplane into two parallel decision hyper-planes, which call multi-label parallel support vector machine (MLPSVM). At the end of the article, MLPSVM is compared with other multi-label learning algorithms. The experimental results show that the algorithm performs well on data sets.
