NDLI: Learning algorithms and dimensionality

Content Provider	IEEE Xplore Digital Library
Author	Sudkamp, T. Hammell II, R.J.
Copyright Year	1997
Description	Author affiliation: Dept. of Comput. Sci., Wright State Univ., Dayton, OH, USA (Sudkamp, T.)
Abstract	Approximation theory based on fuzzy sets provides a tool for modeling complex systems for which only an imprecise or approximate specification is available. In classical modeling, the system relationships are expressed mathematically as a function whose domain consists of the possible inputs to the system and whose range is the appropriate responses. Due to the complexity of the interactions in sophisticated systems, it has become increasingly difficult to construct mathematical models directly from one's knowledge of the system. A fuzzy model provides a functional approximation of the relationships of the underlying system defined by a set of fuzzy rules. The popularity of fuzzy models is attributable to the ability to represent relationships that are too complex or not well enough understood to be directly described by precise mathematical models. The objective of both experimental analysis and propagation application is to determine if the advantages of the two-level model that have been previously demonstrated carry over to more complex problem domains. The results and techniques presented in the paper represent preliminary investigations into the robustness of the learning algorithm in the face of increasing complexity. Ultimately, creating robust learning algorithms will require the combination of many techniques which are dependent upon both type of training data available and the basic properties of the system being modeled.
Starting Page	106
Ending Page	111
File Size	630516
Page Count	6
File Format	PDF
ISBN	0780340787
DOI	10.1109/NAFIPS.1997.624020
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-09-21
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy sets Training data Mathematical model Computer science Laboratories Approximation methods Marine vehicles Data analysis Algorithm design and analysis Error analysis
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
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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

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