多源性数据SVM集成算法研究

doi:10.3969/j.issn.1001-2400.2010.01.024

Abstract

Abstract:

An SVM ensemble learning algorithm based on grouped features is proposed for heterogeneous data. The feature is grouped and trained with different SVM classifiers, and then the final predict labels are obtained by the voting method. The diversity component classifiers with higher classification performance are obtained. Experimental results show that, compared with traditional ensemble learning, this method has the best performance.

Key words: ensemble learning, support vector machine, heterogeneous, medical image

CHANG Tian-tian;LIU Hong-wei;FENG Jun. Support vector machine ensemble learning algorithm research based on heterogeneous data[J].J4, 2010, 37(1): 136-141.

References

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Support vector machine ensemble learning algorithm research based on heterogeneous data

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