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Titlebook: Advances in Self-Organising Maps; Nigel Allinson,Hujun Yin,Jon Slack Conference proceedings 2001 Springer-Verlag London Limited 2001 Data

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楼主: AMUSE
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Recursive learning rules for SOMs,ious possibilities regarding the norm and the direction of the adaptation vectors. The performance and convergence of each rule is evaluated by two criteria: topology preservation and quantization error.
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Canh V. Pham,Huan X. Hoang,Manh M. VuThis paper presents a concept of global distributed information network comprising millions of personal agents acting on the Internet on behalf of their owners.
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ty of Lincolnshire and Humberside, 13 - 15 June, 2001. The University is the newest of England‘s universities but it is situated in the heart of one of our oldest cities - founded by the Romans and overlooked by the towering mass of its medieval cathedral. Primarily Lincoln has always been a centre for the ri978-1-85233-511-3978-1-4471-0715-6
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Towards an information-theoretic approach to kernel-based topographic map formation,mize the differential entropies of the kernel outputs and, at the same time, to minimize the mutual information between these outputs. The learning scheme is based on . learning supplemented with a cooperative/competitive stage to achieve topographically-organized maps. As a potential application, w
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A Statistical Tool to Assess the Reliability of Self-Organizing Maps,sitive than other neural paradigms to problems related to convergence, local minima, etc. This paper introduces objective statistical measures that can be used to assess the stability of the results of SOM, both on the distortion and on the topology preservation points of views.
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A SOM Association Network,processes of the SOMA network are divided into a learning mode and an association mode. In the learning mode, the similar perfect informations are represented by a few units on the competitive layer. In the association mode, when the information, whose parts are lost, is applied to the SOMA network,
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