Treffer: Mixed-norm linear support vector machine
Title:
Mixed-norm linear support vector machine
Authors:
Source:
Neural computing & applications (Print). 23(7-8):2159-2166
Publisher Information:
London: Springer, 2013.
Publication Year:
2013
Physical Description:
print, 19 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Neurology, Neurologie, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Approximation L2, L2 approximation, Aproximación L2, Classification à vaste marge, Vector support machine, Máquina ejemplo soporte, Critère sélection, Selection criterion, Criterio selección, Modélisation, Modeling, Modelización, Optimisation, Optimization, Optimización, Programmation mathématique, Mathematical programming, Programación matemática, Programmation non convexe, Non convex programming, Programación no convexa, Constrained concave―convex procedure, Feature selection, Norm, Support vector machine
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematics, Information School, Renmin University of China, Beijing 100872, China
Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China
College of Science, China Agricultural University, Beijing 100083, China
Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China
College of Science, China Agricultural University, Beijing 100083, China
ISSN:
0941-0643
Rights:
Copyright 2014 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.27907365
Database:
PASCAL Archive
Weitere Informationen
This paper presents a new version of support vector machine (SVM) named l2 ― lp SVM (0 < p < 1) which introduces the lp-norm (0 < p < 1) of the normal vector of the decision plane in the standard linear SVM. To solve the nonconvex optimization problem in our model, an efficient algorithm is proposed using the constrained concave―convex procedure. Experiments with artificial data and real data demonstrate that our method is more effective than some popular methods in selecting relevant features and improving classification accuracy.