NDLI: F-SVC: A simple and fast training algorithm soft margin Support Vector Classification

Content Provider	IEEE Xplore Digital Library
Author	Tohme, M. Lengelle, R.
Copyright Year	2008
Description	Author affiliation: Inst. Charles Delaunay-LM2S, Univ. de Technol. de Troyes, Troyes (Lengelle, R.) \|\| FORENAP Frp, Rouffach (Tohme, M.)
Abstract	Support vector machines have obtained much success in machine learning. But their training require to solve a quadratic optimization problem so that training time increases dramatically with the increase of the training set size. Hence, standard SVM have difficulty in handling large scale problems. In this paper, we present a new fast training algorithm for soft margin support vector classification. This algorithm searches for successive efficient feasible directions. A heuristic for searching the direction maximally correlated with the gradient is applied and the optimum step size of the optimization algorithm is analytically determined. Furthermore the solution, gradient and objective function are recursively obtained. In order to deal with large scale problems, the Gram matrix has not to be stored. Our iterative algorithm fully exploits quadratic functions properties. F-SVC is very simple, easy to implement and able to perform on large data sets.
Starting Page	339
Ending Page	344
File Size	1125557
Page Count	6
File Format	PDF
ISBN	9781424423750
ISSN	15512541
DOI	10.1109/MLSP.2008.4685503
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-16
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Iterative algorithms Support vector machine classification Machine learning algorithms Large-scale systems Algorithm design and analysis Matrix decomposition Fiber reinforced plastics Machine learning Classification algorithms Classification Training algorithm Support Vector Machines
Content Type	Text
Learning Resource Type	Article

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	Mr. Siddhartha Mukherjee siddhartha@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	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray 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

F-SVR: A new learning algorithm for support vector regression

Large margin kernel pocket algorithm

A SVC Iterative Learning Algorithm Based on Sample Selection for Large Samples

Classification using support vector machines with graded resolution

Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

Fast SVM training algorithm with decomposition on very large data sets

A Fast Training Algorithm for Least Squares SVM

A parallel SVM training algorithm on large-scale classification problems

Large Scale Classification with Support Vector Machine Algorithms

F-SVC: A simple and fast training algorithm soft margin Support Vector Classification

Similar Documents

F-SVR: A new learning algorithm for support vector regression

Large margin kernel pocket algorithm

A SVC Iterative Learning Algorithm Based on Sample Selection for Large Samples

Classification using support vector machines with graded resolution

Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms

Fast SVM training algorithm with decomposition on very large data sets

A Fast Training Algorithm for Least Squares SVM

A parallel SVM training algorithm on large-scale classification problems

Large Scale Classification with Support Vector Machine Algorithms

F-SVC: A simple and fast training algorithm soft margin Support Vector Classification