Support vector set selection using pulse-coupled neural networks

A candidate set of support vectors is selected by using pulse-coupled neural networks to reduce computational cost in learning phase for support vector machines (SVMs). The size of the candidate set of support vectors selected this way is smaller than that of the original training samples so that th...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications 2014-08, Vol.25 (2), p.401-410
Main Authors: Li, Yunxia, Yi, Zhang, Lv, Jian Cheng
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science
control theory
systems
Connectionism. Neural networks
Data Mining and Knowledge Discovery
Data processing. List processing. Character string processing
Exact sciences and technology
Image Processing and Computer Vision
Memory organisation. Data processing
Original Article
Pattern recognition. Digital image processing. Computational geometry
Probability and Statistics in Computer Science
Software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A candidate set of support vectors is selected by using pulse-coupled neural networks to reduce computational cost in learning phase for support vector machines (SVMs). The size of the candidate set of support vectors selected this way is smaller than that of the original training samples so that the computation complexity in learning process for support vectors machines based on this candidate set is reduced and the learning process is accelerated. On the other hand, the candidate set of support vectors includes almost all support vectors, and the performance of the SVM based on this candidate set matches the performance when the full training samples are used.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-013-1506-8