NDLI: An effective reject rule for reliability improvement in bank note neuro-classifiers

Content Provider	IEEE Xplore Digital Library
Author	Ahmadi, A. Omatu, S. Kosaka, T.
Copyright Year	2003
Description	Author affiliation: Dept. of Comput. & Syst. Sci., Osaka Prefecture Univ., Japan (Ahmadi, A.; Omatu, S.)
Abstract	In this paper the reliability of bank note neuro-classifiers is investigated and a reject rule is proposed on the basis of probability density function of the input data. The reliability of classification is evaluated through two parameters, which are associated with the winning class probability and the second maximal probability. Then a threshold value is considered to reject the unreliable classifications. As for modeling the non-linear correlation among the data variables and extracting the features, a local principal components analysis (PCA) is applied. The method is tested with a learning vector quantization (LVQ) classifier using 3,600 data samples of various bills of US dollar. The results show that by taking a suitable reject threshold value and also a proper number of regions for the local PCA, the reliability of the system can be improved significantly.
Starting Page	509
Ending Page	516
File Size	406113
Page Count	8
File Format	PDF
ISBN	0780381777
ISSN	10893555
DOI	10.1109/NNSP.2003.1318050
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-17
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Clustering algorithms Feature extraction Topology Neurons Probability density function Data mining Testing Neural networks Robustness
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

Improvement of the reliability of bank note classifier machines

A methodology to evaluate and improve reliability in paper currency neuro-classifiers

A study on evaluating and improving the reliability of bank note neuro-classifiers

A PCA based method for improving the reliability of bank note classifier machines

Bank note classification using neural networks

A reliable method for recognition of paper currency by approach to local PCA

Feature selection method using neural network

Clustering noisy data by a principal feature extraction unsupervised neural network

Handwritten numeral recognition with multiple features and multistage classifiers

An effective reject rule for reliability improvement in bank note neuro-classifiers

Similar Documents

Improvement of the reliability of bank note classifier machines

A methodology to evaluate and improve reliability in paper currency neuro-classifiers

A study on evaluating and improving the reliability of bank note neuro-classifiers

A PCA based method for improving the reliability of bank note classifier machines

Bank note classification using neural networks

A reliable method for recognition of paper currency by approach to local PCA

Feature selection method using neural network

Clustering noisy data by a principal feature extraction unsupervised neural network

Handwritten numeral recognition with multiple features and multistage classifiers

An effective reject rule for reliability improvement in bank note neuro-classifiers