Training the self-organizing feature map using hybrids of genetic and Kohonen methods

M. McInerney; A. Dhawan

Training the self-organizing feature map using hybrids of genetic and Kohonen methods

M. McInerney, A. Dhawan

Research output: Contribution to conference › Paper › peer-review

Abstract

The self-organizing feature map is expected to produce a topologically correct mapping between input and output spaces. This mapping is usually found with the Kohonen learning rule which is sensitive to its parameter values. A poor choice of parameters results in a mapping that may not be topologically correct. In this paper we describe a hybrid algorithm of genetic methods with Kohonen learning that avoids this problem. Experimental results show that this algorithm always results in a topologically correct mapping.

Original language	English (US)
Pages	641-644
Number of pages	4
State	Published - 1994
Externally published	Yes
Event	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: Jun 27 1994 → Jun 29 1994

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Other	Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
City	Orlando, FL, USA
Period	6/27/94 → 6/29/94

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title = "Training the self-organizing feature map using hybrids of genetic and Kohonen methods",

abstract = "The self-organizing feature map is expected to produce a topologically correct mapping between input and output spaces. This mapping is usually found with the Kohonen learning rule which is sensitive to its parameter values. A poor choice of parameters results in a mapping that may not be topologically correct. In this paper we describe a hybrid algorithm of genetic methods with Kohonen learning that avoids this problem. Experimental results show that this algorithm always results in a topologically correct mapping.",

author = "M. McInerney and A. Dhawan",

year = "1994",

language = "English (US)",

pages = "641--644",

note = "Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) ; Conference date: 27-06-1994 Through 29-06-1994",

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AU - McInerney, M.

AU - Dhawan, A.

PY - 1994

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N2 - The self-organizing feature map is expected to produce a topologically correct mapping between input and output spaces. This mapping is usually found with the Kohonen learning rule which is sensitive to its parameter values. A poor choice of parameters results in a mapping that may not be topologically correct. In this paper we describe a hybrid algorithm of genetic methods with Kohonen learning that avoids this problem. Experimental results show that this algorithm always results in a topologically correct mapping.

AB - The self-organizing feature map is expected to produce a topologically correct mapping between input and output spaces. This mapping is usually found with the Kohonen learning rule which is sensitive to its parameter values. A poor choice of parameters results in a mapping that may not be topologically correct. In this paper we describe a hybrid algorithm of genetic methods with Kohonen learning that avoids this problem. Experimental results show that this algorithm always results in a topologically correct mapping.

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UR - http://www.scopus.com/inward/citedby.url?scp=0028734323&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0028734323

SP - 641

EP - 644

T2 - Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)

Y2 - 27 June 1994 through 29 June 1994

ER -

Training the self-organizing feature map using hybrids of genetic and Kohonen methods

Abstract

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