Winner-relaxing and winner-enhancing Kohonen maps: Maximal mutual information from enhancing the winner
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چکیده
منابع مشابه
Winner-relaxing and winner-enhancing Kohonen maps: Maximal mutual information from enhancing the winner
The magnification behaviour of a generalized family of self-organizing feature maps, the Winner Relaxing and Winner Enhancing Kohonen algorithms is analyzed by the magnification law in the one-dimensional case, which can be obtained analytically. The Winner-Enhancing case allows to acheive a magnification exponent of one and therefore provides optimal mapping in the sense of information theory....
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Self-Organizing Maps are models for unsupervised representation formation of cortical receptor fields by stimuli-driven self-organization in laterally coupled winner-take-all feedforward structures. This paper discusses modifications of the original Kohonen model that were motivated by a potential function, in their ability to set up a neural mapping of maximal mutual information. Enhancing the...
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We calculate analytically the magnification behaviour of a generalized family of self-organizing feature maps inspired by a variant introduced by Kohonen in 1991, denoted here as Winner Relaxing Kohonen algorithm, which is shown here to have a magnification exponent of 4/7. Motivated by the observation that a modification of the learning rule for the winner neuron influences the magnification l...
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A new family of self-organizing maps, the Winner-Relaxing Kohonen Algorithm, is introduced as a generalization of a variant given by Kohonen in 1991. The magnification behaviour is calculated analytically. For the original variant a magnification exponent of 4/7 is derived; the generalized version allows to steer the magnification in the wide range from exponent 1/2 to 1 in the one-dimensional ...
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We transfer the idea of winner relaxing learning from the self-organizing map to the neural gas to enable magnification control independently of the shape of the data distribution.
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ژورنال
عنوان ژورنال: Complexity
سال: 2003
ISSN: 1076-2787,1099-0526
DOI: 10.1002/cplx.10084