关键词: 特征加权, 支持向量机, 核学习

Abstract: A kernel learning algorithm is proposed based on feature weight. As in the classification task, each feature of data sample is not taken the same contribution, some features are very related to the classification task, it should be given more attention. The proposed algorithm integrates the advantages of multiple kernels learning, which is a weighted combination of different kernel functions, but at a lower computational complexity required. Experimental results show that the proposed algorithm, compared to the multiple kernel learning and support vector machines, obtains the state of art results, and its computational complexity is slightly higher than support vector machines, but with far less than multiple kernels learning algorithm.

Key words: feature weight learning, support vector machine, kernel learning