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Prediction of Rolling Force Based on a Fusion of Extreme Learning Machine and Self Learning Model of Rolling Force Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Binary Tree Construction of Multiclass Pinball SVM Via Farthest Centroid Selection

Authors : Qiangkui Leng, Fude Liu, Yuping Qin

Published in: Advances in Intelligent Systems and Interactive Applications

Publisher: Springer International Publishing

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Abstract

The paper generalizes PinSVM to multiclass version by using binary tree structure. At each internal node, all inherited classes are first divided into two groups via farthest centroid selection. Then, PinSVM is constructed between two groups. When each group contains only one class, the leaf node can be identified. The experimental results show that binary-tree multiclass PinSVM is very competitive with one-versus-one PinSVM and one-versus-one SVM. Especially in terms of computational time, it has clear superiority than them.

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previous chapter Impact Analysis of Geometry Parameters of Buoy on the Pitching Motion Mechanism and Power Response for Multi-section Wave Energy Converter
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Metadata
Title
Binary Tree Construction of Multiclass Pinball SVM Via Farthest Centroid Selection
Authors
Qiangkui Leng
Fude Liu
Yuping Qin
Copyright Year
2018
Book
Advances in Intelligent Systems and Interactive Applications

Print ISBN: 978-3-319-69095-7

Electronic ISBN: 978-3-319-69096-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-69096-4_45

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