NDLI: A novel learning algorithm for data classification with radial basis function networks

Content Provider	IEEE Xplore Digital Library
Author	Yen-Jen Oyang Shien-Ching Hwang Yu-Yen Ou Chien-Yu Chen Zhi-Wei Chen
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ., Taipei, Taiwan (Yen-Jen Oyang; Shien-Ching Hwang; Yu-Yen Ou; Chien-Yu Chen; Zhi-Wei Chen)
Abstract	This paper proposes a novel learning algorithm for constructing data classifiers with radial basis function (RBF) networks. The RBF networks constructed with the proposed learning algorithm generally are able to deliver the same level of classification accuracy as the support vector machines (SVM). One important advantage of the proposed learning algorithm, in comparison with the support vector machines, is that the proposed learning algorithm normally takes far less time to figure out optimal parameter values with cross validation. A comparison with the SVM is of interest, because it has been shown in a number of recent studies that the SVM generally is able to deliver higher level of accuracy than the other existing data classification algorithms. The proposed learning algorithm works by constructing one RBF network to approximate the probability density function of each class of objects in the training data set. The main distinction of the proposed learning algorithm is how it exploits local distributions of the training samples in determining the optimal parameter values of the basis functions. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	1021
Ending Page	1026
File Size	653979
Page Count	6
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1198215
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Classification algorithms Radial basis function networks Support vector machines Support vector machine classification Machine learning Training data Probability density function Computer science Neural networks Application software
Content Type	Text
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	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

Data classification with radial basis function networks based on a novel kernel density estimation algorithm

Novel Data Classification Method Based on Radial Basis Function Networks

A Novel Learning Algorithm for Data Classification with Radial Basis Function Networks (2001)

A novel radial basis function network classifier with centers set by hierarchical clustering

Extreme learning machine: RBF network case

The Training of Radial Basis Function Network Classifier with Cooperative Clustering

Paper: tnn ce485 data classification with radial basis function networks based on a novel kernel density estimation algorithm.

An efficient learning algorithm for function approximation with radial basis function networks

Nelder-mead enhanced extreme learning machine

A novel learning algorithm for data classification with radial basis function networks

Similar Documents

Data classification with radial basis function networks based on a novel kernel density estimation algorithm

Novel Data Classification Method Based on Radial Basis Function Networks

A Novel Learning Algorithm for Data Classification with Radial Basis Function Networks (2001)

A novel radial basis function network classifier with centers set by hierarchical clustering

Extreme learning machine: RBF network case

The Training of Radial Basis Function Network Classifier with Cooperative Clustering

Paper: tnn ce485 data classification with radial basis function networks based on a novel kernel density estimation algorithm.

An efficient learning algorithm for function approximation with radial basis function networks

Nelder-mead enhanced extreme learning machine

A novel learning algorithm for data classification with radial basis function networks