NDLI: Region-mapping neural network model for pattern recognition

Content Provider	IEEE Xplore Digital Library
Author	Yan-Lai Li Kuan-Quan Wang Zhang, D.
Copyright Year	2002
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Harbin Inst. of Technol., China (Yan-Lai Li; Kuan-Quan Wang)
Abstract	In general, the process for multilayer feedforward neural network in pattern recognition is composed of two phases: training and classifying. The aim of the training phase is to make it possible for the network output to meet the desired output given by the training patterns. It demands a map of point to point, which is so strict that it often causes the criterion inconsistence between training and classifying. Consequently, the recognition rate would be decreased. The region-mapping model has changed the output space from one point to a certain supervisor region so that it has overcome the shortcoming of the inconsistent problem between training and testing as a common multilayer perceptron (MLP) does. Furthermore, it can save much of the computing time by mapping the input data to an output area rather than an output point. This paper presents a region-mapping model with quarter hyper globe as a supervisor region. The gradient decent algorithm is applied to this model. In order to illustrate the effect of our propounded model, a handwritten letter recognition problem was carried out in an experiment. Moment invariant features are used as input parameters. The simulation results show that the region-mapping model has much better characteristics than those common multiplayer perceptrons. Also, the quarter hyper globe rule is more reasonable than the hypercube one.
Sponsorship	Hebei Univ. IEEE Syst., Man & Cybernetics Tech. Committe on Cybernetics
Starting Page	1541
Ending Page	1545
File Size	299283
Page Count	5
File Format	PDF
ISBN	0780375084
DOI	10.1109/ICMLC.2002.1167468
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Pattern recognition Multi-layer neural network Computer science Computer networks Electronic mail Feedforward neural networks Testing Nonhomogeneous media Multilayer perceptrons
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

GMDP: a novel unified neuron model for multilayer feedforward neural networks

CBP networks as a generalized neural model

Classification boundaries and gradients of trained multilayer perceptrons

A step towards the frontier between one-hidden-layer and two-hidden-layer neural networks

Adaptive high order neural trees for pattern recognition

An efficient multilayer quadratic perceptron for pattern classification and function approximation

Fast training algorithms for multilayer neural nets

Spurious states detection and basin describing in feedforward neural networks

Two-level learning algorithm for multilayer neural networks

Region-mapping neural network model for pattern recognition

Similar Documents

GMDP: a novel unified neuron model for multilayer feedforward neural networks

CBP networks as a generalized neural model

Classification boundaries and gradients of trained multilayer perceptrons

A step towards the frontier between one-hidden-layer and two-hidden-layer neural networks

Adaptive high order neural trees for pattern recognition

An efficient multilayer quadratic perceptron for pattern classification and function approximation

Fast training algorithms for multilayer neural nets

Spurious states detection and basin describing in feedforward neural networks

Two-level learning algorithm for multilayer neural networks

Region-mapping neural network model for pattern recognition