Nonlinear Feature Selection by Relevance Feature Vector Machine 1st Edition by Haibin Cheng, Haifeng Chen, Guofei Jiang, Kenji Yoshihira 9783540734987

Nonlinear Feature Selection by Relevance Feature Vector Machine 1st Edition by Haibin Cheng, Haifeng Chen, Guofei Jiang, Kenji Yoshihira – Ebook PDF Instant Download/Delivery. 9783540734987

Full download Nonlinear Feature Selection by Relevance Feature Vector Machine 1st Edition after payment

Product details:

ISBN 13: 9783540734987

Author: Haibin Cheng, Haifeng Chen, Guofei Jiang; Kenji Yoshihira

Support vector machine (SVM) has received much attention in feature selection recently because of its ability to incorporate kernels to discover nonlinear dependencies between features. However it is known that the number of support vectors required in SVM typically grows linearly with the size of the training data set. Such a limitation of SVM becomes more critical when we need to select a small subset of relevant features from a very large number of candidates. To solve this issue, this paper proposes a novel algorithm, called the ‘relevance feature vector machine’(RFVM), for nonlinear feature selection. The RFVM algorithm utilizes a highly sparse learning algorithm, the relevance vector machine (RVM), and incorporates kernels to extract important features with both linear and nonlinear relationships. As a result, our proposed approach can reduce many false alarms, e.g. including irrelevant features, while still maintain good selection performance. We compare the performances between RFVM and other state of the art nonlinear feature selection algorithms in our experiments. The results confirm our conclusions.

Nonlinear Feature Selection by Relevance Feature Vector Machine 1st Table of contents:

1 Introduction

2 The Potential Support Vector Machine

3 P-SVM Discussion and Redundancy Control

4 Nonlinear P-SVM Feature Selection

5 Experiment

6 Conclusion 

People also search for Nonlinear Feature Selection by Relevance Feature Vector Machine 1st: 

non linear feature selection

feature selection for nonlinear data

r nonlinear regression

r nonlinear fit