Bucket 发表于 2025-3-23 10:52:50

http://reply.papertrans.cn/87/8646/864516/864516_11.png

Perceive 发表于 2025-3-23 14:10:42

Variants of SOM,.. There seems to exist an indefinite number of ways to define the matching of an input occurrence with the internal representations, and even the neighborhood of a unit can be defined in many ways. It is neither necessary to define the corrections as gradient steps in the parameter space: improveme

locus-ceruleus 发表于 2025-3-23 21:26:07

Learning Vector Quantization, are . clustering and learning methods, LVQ describes .. On the other hand, unlike in SOM, no neighborhoods around the “winner” are defined during learning in the basic LVQ, whereby also no spatial order of the codebook vectors is expected to ensue.

oxidize 发表于 2025-3-24 01:28:32

http://reply.papertrans.cn/87/8646/864516/864516_14.png

Chivalrous 发表于 2025-3-24 06:02:21

http://reply.papertrans.cn/87/8646/864516/864516_15.png

VOK 发表于 2025-3-24 08:50:06

Hardware for SOM,real time, most ANN algorithms can be implemented by pure software, especially if contemporary workstation computers are available. However, especially in real-time pattern recognition or robotics applications one might need special co-processor boards or even “neurocomputers.” For really large prob

Mere仅仅 发表于 2025-3-24 14:17:53

An Overview of SOM Literature,000 at the moment of writing. In the second edition of IS30, this figure was still about 1500. One of the problems therefore was where to draw the line in referring to the literature. It would have been absurd to add to this edition some 150 pages of extra text relating to new references. As most of

Postmenopause 发表于 2025-3-24 18:43:21

http://reply.papertrans.cn/87/8646/864516/864516_18.png

抵消 发表于 2025-3-24 19:45:33

http://reply.papertrans.cn/87/8646/864516/864516_19.png

Dysarthria 发表于 2025-3-24 23:46:09

http://reply.papertrans.cn/87/8646/864516/864516_20.png
页: 1 [2] 3 4 5
查看完整版本: Titlebook: Self-Organizing Maps; Teuvo Kohonen Book 2001Latest edition Springer-Verlag Berlin Heidelberg 2001 Adaptive and Learning Networks.Adaptive