一种基于RVM回归的分类方法

Abstract

Abstract:

The support vector machine is a state-of-the-art technique for regression and classification.However,it suffers from a number of disadvantages,notably the absence of probabilistic outputs,the requirement to estimate a trade-off parameter C and the need to utilize ‘Mercer’ kernel functions.The Relevance Vector Machine suffers from none of the above disadvantages,and obtains comparable generalization performance.The RVM requires dramatically fewer kernel functions.In this paper,we introduce a new classification method based on RVM regression,which is called RVRC.Experiments demonstrate its practicability.

Key words: RVM;SVM;classification;regression;RVRC

CLC Number:

TP18

WANG Li-Kun, YANG Xin-Feng. A Classification Method Based on RVM Regression[J]., 2011, 24(5): 14-.

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A Classification Method Based on RVM Regression

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