A kernel level composition of multiple local classifiers for nonlinear classification
A kernel level composition of multiple local classifiers for nonlinear classification
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DOI：
10.1109/ijcnn.2016.7727696
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Weite Li;Bo Zhou;Jinglu Hu
中科院分区：
文献类型：
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作者：
Weite Li;Bo Zhou;Jinglu Hu
在过去的几十年里，基于核函数的机器学习算法已经得到了广泛的研究，并成功地应用于各种现实世界的任务中。本文提出了一种核级组合方法，将多个局部分类器（核）嵌入到一个核函数中，从而获得更灵活的数据依赖核。由于这种复合核是由多个局部分类器内插多个局部门控函数组成的，因此本文还引入了一个特定的学习过程来预先确定它们的参数。实验结果验证了本文的两个主要观点。首先，所引入的学习过程能够有效地检测出局部信息，这对于局部门控函数参数的预先确定至关重要。其次，所提出的复合核具有提高分类性能的能力。
Kernel functions based machine learning algorithms have been extensively studied over the past decades with successful applications in a variety of real-world tasks. In this paper, we formulate a kernel level composition method to embed multiple local classifiers (kernels) into one kernel function, so as to obtain a more flexible data-dependent kernel. Since such composite kernels are composed by multiple local classifiers interpolated with several localizing gating functions, a specific learning process is also introduced in this paper to pre-determine their parameters. Experimental results are provided to validate two major perspectives of this paper. Firstly, the introduced learning process is effective to detect local information, which is essential for the parameter pre-determination of the localizing gating functions. Secondly, the proposed composite kernel has a capacity to improve classification performance.