PREDICTIVE INFORMATION CRITERIA FOR ROBUST RELEVANCE VECTOR REGRESSION MODELS
PREDICTIVE INFORMATION CRITERIA FOR ROBUST RELEVANCE VECTOR REGRESSION MODELS
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DOI：
10.5109/2233862
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Kazuki Matsuda;Shuichi Kawano;S. Konishi
中科院分区：
文献类型：
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作者：
Kazuki Matsuda;Shuichi Kawano;S. Konishi
The relevance vector regression (RVR) is a Bayesian nonlinear regression procedure whose model is expressed in terms of kernel functions, like the support vector regression (SVR). In order to overcome the sensitivity to outliers of the RVR, the robust relevance vector regression (RRVR) procedures have been proposed. A crucial issue in the model building process of the RRVR is the choice of kernel parameters. The selection of these parameters can be viewed as a model selection and evaluation problem. In this paper, we derive a model selection criterion for the Bayesian predictive distribution of the RRVR models. Monte Carlo experiments and real data analysis show that our model selection criterion performs well in various situations.
