S. Conforto et al., High-quality compression of echographic images by neural networks and vector quantization, Med. Biol. Eng. Comput. 33 (5) (1995) 695}698, ISSN 0140-0118.  Home/Search   Document Not in Database   Summary   Related Articles

....and decoder is often designated as the training set, and the rst M input vectors of the training data set are normally used to initialize all the neurones. With this general structure, various learning algorithms have been designed and developed such as Kohonen s self organizing feature mapping [10,13,18,33,52,70], competitive learning [1,54,55,65] frequency sensitive competitive learning [1,10] fuzzy competitive learning [11,31,32] general learning [25,49] and distortion equalized fuzzy competitive learning [7] and PVQ (predictive VQ) neural networks [46] Let W (t) be the weight vector of the ith ....

....and z is its output. A so called under utilization problem [1,11] occurs in competitive learning which means some of the neurones are left out of the learning process and never win the competition. Various schemes are developed to tackle this problem. Kohonen selforganizing neural network [10,13,18] overcomes the problem by updating the winning neurone as well as those neurones in its neighbourhood. Frequency sensitive competitive learning algorithm addresses the problem by keeping a record of how frequent each neurone is the winner to maintain that all neurones in the network are updated ....

S. Conforto et al., High-quality compression of echographic images by neural networks and vector quantization, Med. Biol. Eng. Comput. 33 (5) (1995) 695}698, ISSN 0140-0118.

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