SVR+RVR: A ROBUST SPARSE KERNEL METHOD FOR REGRESSION

Author:

ZHANG GAI-YING1,GUO GAO2,ZHANG JIANG-SHE1

Affiliation:

1. Institute for Information Science and System Science, Faculty of Science, Xi'an Jiaotong University, Xi'an 710049, China

2. School of Science, Xi'an University of Technology, Xi'an 710048, China

Abstract

Support vector machine (SVM) and relevance vector machine (RVM) are two state of the art kernel learning methods. But both methods have some disadvantages: although SVM is very robust against outliers, it makes unnecessarily liberal use of basis functions since the number of support vectors required typically grows linearly with the size of the training set; on the other hand the solution of RVM is astonishingly sparse, but its performance deteriorates significantly when the observations are contaminated by outliers. In this paper, we present a combination of SVM and RVM for regression problems, in which the two methods are concatenated: firstly, we train a support vector regression (SVR) machine on the full training set; then a relevance vector regression (RVR) machine is trained only on a subset consisting of support vectors, but whose target values are replaced by the predictions of SVR. Using this combination, we overcome the drawbacks of SVR and RVR. Experiments demonstrate SVR+RVR is both very sparse and robust.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Vibration-Based Outlier Detection on High Dimensional Data;International Journal on Artificial Intelligence Tools;2016-06

2. An Integrated Model Combined ARIMA, EMD with SVR for Stock Indices Forecasting;International Journal on Artificial Intelligence Tools;2016-04

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