NDLI: Comparing support vector machines with Gaussian kernels to radial basis function classifiers

Content Provider	IEEE Xplore Digital Library
Author	Scholkopf, B. Kah-Kay Sung Burges, C.J.C. Girosi, F. Niyogi, P. Poggio, T. Vapnik, V.
Copyright Year	1991
Abstract	The support vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights, and threshold that minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by X-means clustering, and the weights are computed using error backpropagation. We consider three machines, namely, a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the United States postal service database of handwritten digits, the SV machine achieves the highest recognition accuracy, followed by the hybrid system. The SV approach is thus not only theoretically well-founded but also superior in a practical application.
Sponsorship	IEEE Signal Processing Society
Starting Page	2758
Ending Page	2765
Page Count	8
File Size	192595
File Format	PDF
ISSN	1053587X
Volume Number	45
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Kernel Support vector machine classification Machine learning Statistical learning Polynomials Neural networks Clustering algorithms Backpropagation algorithms Upper bound
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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

Support-vector-based fuzzy neural network for pattern classification

An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels

Radial basis function kernel optimization for Support Vector Machine classifiers

Kernel-based methods for hyperspectral image classification

Time-Frequency Learning Machines

On the Improvement of Support Vector Techniques for Clustering by Means of Whitening Transform

Kernel Antenna Array Processing

Nonlinear System Identification With Composite Relevance Vector Machines

Support-vector networks

Comparing support vector machines with Gaussian kernels to radial basis function classifiers

Similar Documents

Support-vector-based fuzzy neural network for pattern classification

An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels

Radial basis function kernel optimization for Support Vector Machine classifiers

Kernel-based methods for hyperspectral image classification

Time-Frequency Learning Machines

On the Improvement of Support Vector Techniques for Clustering by Means of Whitening Transform

Kernel Antenna Array Processing

Nonlinear System Identification With Composite Relevance Vector Machines

Support-vector networks

Comparing support vector machines with Gaussian kernels to radial basis function classifiers