NDLI: Applying a novel decision rule to the sphere-structured support vector machines algorithm

Content Provider	Springer Nature Link
Author	Gu, Lei Wu, Hui zhong
Copyright Year	2008
Abstract	The traditional sphere-structured support vector machines algorithm is one of the learning methods. It can partition the training samples space by means of constructing the spheres with the minimum volume covering all training samples of each pattern class in high-dimensional feature space. However, the decision rule of the traditional sphere-structured support vector machines cannot assign ambiguous sample points such as some encircled by more than two spheres to valid class labels. Therefore, the traditional sphere-structured support vector machines is insufficient for obtaining the better classification performance. In this article, we propose a novel decision rule applied to the traditional sphere-structured support vector machines. This new decision rule significantly improves the performance of labeling ambiguous points. Experimental results of seven real datasets show the traditional sphere-structured support vector machines based on this new decision rule can not only acquire the better classification accuracies than the traditional sphere-structured support vector machines but also achieve the comparable performance to the classical support vector machines.
Starting Page	275
Ending Page	282
Page Count	8
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	18
Issue Number	3
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-02-28
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Pattern classification Sphere-structured support vector machines Decision rule Kernel functions Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Applying a novel decision rule to the sphere-structured support vector machines algorithm

Applying a novel decision rule to the sphere-structured support vector machines algorithm

Polynomial kernel adaptation and extensions to the SVM classifier learning

Pattern classification with mixtures of weighted least-squares support vector machine experts

Imbalanced classification using support vector machine ensemble

Methods for the combination of kernel matrices within a support vector framework

Twin Support Vector Machines for Pattern Classification

A theoretical and empirical analysis of support vector machine methods for multiple-instance classification

Sparse kernel SVMs via cutting-plane training

Applying a novel decision rule to the sphere-structured support vector machines algorithm

Similar Documents

Applying a novel decision rule to the sphere-structured support vector machines algorithm

Applying a novel decision rule to the sphere-structured support vector machines algorithm

Polynomial kernel adaptation and extensions to the SVM classifier learning

Pattern classification with mixtures of weighted least-squares support vector machine experts

Imbalanced classification using support vector machine ensemble

Methods for the combination of kernel matrices within a support vector framework

Twin Support Vector Machines for Pattern Classification

A theoretical and empirical analysis of support vector machine methods for multiple-instance classification

Sparse kernel SVMs via cutting-plane training

Applying a novel decision rule to the sphere-structured support vector machines algorithm