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Optimal Scheduling of the Leaves of a Tree and the SVO Frequencies of Languages Read chapter Read first chapter

2022 | OriginalPaper | Chapter

Bounds for Sparse Solutions of K-SVCR Multi-class Classification Model

Authors : Hossein Moosaei, Milan Hladík

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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Abstract

The support vector classification-regression machine for k-class classification (K-SVCR) is a novel multi-class classification approach based on the “1-versus-1-versus-rest” structure. In this work, we suggested an efficient model by proposing the p-norm \((0<p< 1)\) instead of the 2-norm for the regularization term in the objective function of K-SVCR that can be used for feature selection. We derived lower bounds for the absolute value of nonzero entries in every local optimal solution of the p-norm based model. Also, we provided upper bounds for the number of nonzero components of the optimal solutions. We explored the link between solution sparsity, regularization parameters, and the p-choice.

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Metadata
Title
Bounds for Sparse Solutions of K-SVCR Multi-class Classification Model
Authors
Hossein Moosaei
Milan Hladík
Copyright Year
2022
Book
Learning and Intelligent Optimization

Print ISBN: 978-3-031-24865-8

Electronic ISBN: 978-3-031-24866-5

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-24866-5_11

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