{"product_id":"variants-of-self-organizing-maps-von-chao-huang-wang-chung-nan-lee-und-chaur-heh-hsieh","title":"Variants of Self-Organizing Maps","description":"\u003cp\u003eThe self-organizing map (SOM) is an unsupervised  learning algorithm which has been successfully  applied to various applications. In the last several  decades, there have been variants of SOM used in many  application domains. In this work, two new SOM  algorithms are developed for image quantization and  compression.  The first algorithm is a sample-size adaptive SOM  algorithm that can be used for color quantization of  images to adapt to the variations of network  parameters and training sample size. Based on the  sample-size adaptive self-organizing map, we use the  sampling ratio of training data, rather than the  conventional weight change between adjacent sweeps,  as a stop criterion. As a result, it can  significantly speed up the learning process.  The second algorithm is a novel classified SOM method  for edge preserving quantization of images using an  adaptive subcodebook and weighted learning rate. The  subcodebook sizes of two classes are automatically  adjusted in training iterations that can be estimated  incrementally. The proposed weighted learning rate  updates the neuron efficiently no matter how large  the weighting factor is.\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9783838324364\"\u003e\u003ch3\u003eApplications in Image Quantization and Compression\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9783838324364","offer_id":39498923835485,"sku":"9783838324364","price":49.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/17b80f9a-f26c-427c-9568-9bca5ac13f4d.jpg?v=1769669626","url":"https:\/\/shop.autorenwelt.de\/products\/variants-of-self-organizing-maps-von-chao-huang-wang-chung-nan-lee-und-chaur-heh-hsieh","provider":"Autorenwelt Shop","version":"1.0","type":"link"}