NDLI: Minimum entropy methods in neural networks: competition and selective responses by entropy minimization

Content Provider	IEEE Xplore Digital Library
Author	Kamimura, R.
Copyright Year	1993
Description	Author affiliation: Inf. Sci. Lab., Tokai Univ., Kanagawa, Japan (Kamimura, R.)
Abstract	A method of entropy minimization is proposed to address the author's hypothesis that multiple aspects of the learning can be described by the entropy minimization. To demonstrate the hypothesis, the competition among units such as a winner-take-all rule and selective responses is examined. In the winner-take-all rule, units compete with each other, and only one unit wins the competition, and is turned on. The process can be described by entropy minimization. In a state of minimum entropy, only one unit is turned on, while all the other units are turned off. Neurons selectively respond to specific patterns. These selective responses can also be described by using an entropy function. In a state of minimum entropy, a unit responds only to one specific pattern, while in a state of maximum entropy, the unit responds to all the patterns uniformly. Formulating two kinds of entropy function for the competition and selection, the method is applied to several problems. In all cases, it is confirmed that the entropy method enables units to compete with each other and could significantly improve the selectivity of units.
Starting Page	219
Ending Page	225
File Size	604418
Page Count	7
File Format	PDF
ISBN	0780309995
DOI	10.1109/ICNN.1993.298560
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-03-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Entropy Intelligent networks Neural networks Minimization methods Neurons Information science Laboratories Proposals Computer vision Detectors
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

D-entropy minimization: integration of mutual information maximization and minimization

Principal hidden unit analysis: generation of simple networks by minimum entropy method

Inheritance of information in multi-layer sigma-pi neural networks

Conditional entropy minimization in neural network classifiers

D-entropy controller for interpretation and generalization

Generation of organized internal representation in recurrent neural networks

Information maximization for feature detection and pattern classification by autoencoders

Kernel feature detector: extracting kernel features by minimizing /spl alpha/-information

Simultaneous information maximization and minimization

Minimum entropy methods in neural networks: competition and selective responses by entropy minimization

Similar Documents

D-entropy minimization: integration of mutual information maximization and minimization

Principal hidden unit analysis: generation of simple networks by minimum entropy method

Inheritance of information in multi-layer sigma-pi neural networks

Conditional entropy minimization in neural network classifiers

D-entropy controller for interpretation and generalization

Generation of organized internal representation in recurrent neural networks

Information maximization for feature detection and pattern classification by autoencoders

Kernel feature detector: extracting kernel features by minimizing /spl alpha/-information

Simultaneous information maximization and minimization

Minimum entropy methods in neural networks: competition and selective responses by entropy minimization