Supervised and unsupervised fuzzy-adaptive Hamming net

Paul S. Wu; Ming Li

doi:10.1016/S0031-3203(98)00049-1

Supervised and unsupervised fuzzy-adaptive Hamming net

Paul S. Wu
, Ming Li

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

5 Citations (Scopus)

Abstract

A new fuzzy-adaptive Hamming net with supervised and unsupervised learning is proposed in this paper. The new neural model is derived from fuzzy-adaptive Hamming net and retains the advantage of deleting the search time that is a potential serious problem for ART model. The new neural model sets different vigilance parameters for different clusters (hyper-rectangles will be used in this paper). This feature makes input pattern classification more efficient when different hyper-rectangles occupy different sizes of characteristic spaces. The newly proposed supervised and unsupervised learning rule which only establishes non-cross overlapped hyper-rectangles, does not affect other hyper-rectangles during expansion and makes the classification more stable. In addition, the new learning rule allows the creation of nested hyper-rectangles in order to resolve the problem of non-convex input patterns. Simulations of the new net to palm prints recognition have been performed and good performance has been demonstrated.
© 1999 Pattern Recognition Society

Original language	English
Pages (from-to)	1801-1816
Journal	Pattern Recognition
Volume	32
Issue number	10
DOIs	https://doi.org/10.1016/S0031-3203(98)00049-1
Publication status	Published - Oct 1999
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

The authors would like to acknowledge the support provided by Croucher Foundation under the project number 9050044.

Research Keywords

Adaptive Hamming nets
ART
Fuzzy
Pattern recognition
Supervised
Unsupervised

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@article{b968df4a8dec43648ac3eae2030220ed,

title = "Supervised and unsupervised fuzzy-adaptive Hamming net",

abstract = "A new fuzzy-adaptive Hamming net with supervised and unsupervised learning is proposed in this paper. The new neural model is derived from fuzzy-adaptive Hamming net and retains the advantage of deleting the search time that is a potential serious problem for ART model. The new neural model sets different vigilance parameters for different clusters (hyper-rectangles will be used in this paper). This feature makes input pattern classification more efficient when different hyper-rectangles occupy different sizes of characteristic spaces. The newly proposed supervised and unsupervised learning rule which only establishes non-cross overlapped hyper-rectangles, does not affect other hyper-rectangles during expansion and makes the classification more stable. In addition, the new learning rule allows the creation of nested hyper-rectangles in order to resolve the problem of non-convex input patterns. Simulations of the new net to palm prints recognition have been performed and good performance has been demonstrated. {\textcopyright} 1999 Pattern Recognition Society",

keywords = "Adaptive Hamming nets, ART, Fuzzy, Pattern recognition, Supervised, Unsupervised",

author = "Wu, \{Paul S.\} and Ming Li",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].",

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AU - Wu, Paul S.

AU - Li, Ming

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 1999/10

Y1 - 1999/10

N2 - A new fuzzy-adaptive Hamming net with supervised and unsupervised learning is proposed in this paper. The new neural model is derived from fuzzy-adaptive Hamming net and retains the advantage of deleting the search time that is a potential serious problem for ART model. The new neural model sets different vigilance parameters for different clusters (hyper-rectangles will be used in this paper). This feature makes input pattern classification more efficient when different hyper-rectangles occupy different sizes of characteristic spaces. The newly proposed supervised and unsupervised learning rule which only establishes non-cross overlapped hyper-rectangles, does not affect other hyper-rectangles during expansion and makes the classification more stable. In addition, the new learning rule allows the creation of nested hyper-rectangles in order to resolve the problem of non-convex input patterns. Simulations of the new net to palm prints recognition have been performed and good performance has been demonstrated. © 1999 Pattern Recognition Society

AB - A new fuzzy-adaptive Hamming net with supervised and unsupervised learning is proposed in this paper. The new neural model is derived from fuzzy-adaptive Hamming net and retains the advantage of deleting the search time that is a potential serious problem for ART model. The new neural model sets different vigilance parameters for different clusters (hyper-rectangles will be used in this paper). This feature makes input pattern classification more efficient when different hyper-rectangles occupy different sizes of characteristic spaces. The newly proposed supervised and unsupervised learning rule which only establishes non-cross overlapped hyper-rectangles, does not affect other hyper-rectangles during expansion and makes the classification more stable. In addition, the new learning rule allows the creation of nested hyper-rectangles in order to resolve the problem of non-convex input patterns. Simulations of the new net to palm prints recognition have been performed and good performance has been demonstrated. © 1999 Pattern Recognition Society

KW - Adaptive Hamming nets

KW - ART

KW - Fuzzy

KW - Pattern recognition

KW - Supervised

KW - Unsupervised

UR - https://www.scopus.com/pages/publications/0032826637

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0032826637&origin=recordpage

U2 - 10.1016/S0031-3203(98)00049-1

DO - 10.1016/S0031-3203(98)00049-1

M3 - RGC 21 - Publication in refereed journal

SN - 0031-3203

VL - 32

SP - 1801

EP - 1816

JO - Pattern Recognition

JF - Pattern Recognition

IS - 10

ER -

Supervised and unsupervised fuzzy-adaptive Hamming net

Abstract

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Research Keywords

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