Towards minimizing the energy of slack variables for binary classification
File(s)
Accepted version
Author(s)
Kotti, M
Diamantaras, Konstantinos
Type
Conference Paper
Abstract
This paper presents a binary classification algorithm that is based on the minimization of the energy of slack variables, called the Mean Squared Slack (MSS). A novel kernel extension is proposed which includes the withholding of just a subset of input patterns that are misclassified during training. The later leads to a time and memory efficient system that converges in a few iterations. Two datasets are exploited for performance evaluation, namely the adult and the vertebral column dataset. Experimental results demonstrate the effectiveness of the proposed algorithm with respect to computation time and scalability. Accuracy is also high. In specific, it equals 84.951% for the adult dataset and 91.935%, for the vertebral column dataset, outperforming state-of-the-art methods. © 2012 EURASIP.
Date Issued
2012-08
Citation
20th European Signal Processing Conference, 2012, pp.644-648
ISSN
2076-1465
Publisher
EURASIP
Start Page
644
End Page
648
Journal / Book Title
20th European Signal Processing Conference
Copyright Statement
© EURASIP 2012
Description
20.09.13 KB. Ok to add to spiral, author says conference already available online.
Identifier
http://www.eurasip.org/Proceedings/Eusipco/Eusipco2012/Conference/authors.html
Source
EUSIPCO 2012
Source Place
Bucharest, Romania
Start Date
2012-08-27
Finish Date
2012-08-31
Coverage Spatial
Bucharest, Romania