gnd:ann
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| === Vektorová kvantizácia=== | === Vektorová kvantizácia=== | ||
| + | citat z wolframu: | ||
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| + | Another neural network type that has some similarities to the unsupervised one is the Vector Quantization (VQ) network, whose intended use is classification. Like unsupervised networks, the VQ network is based on a set of codebook vectors. Each class has a subset of the codebook vectors associated to it, and a data vector is classified to be in the class to which the closest codebook vector belongs. In the neural network literature, the codebook vectors are often called the neurons of the VQ network. | ||
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| + | Each of the codebook vectors has a part of the space " | ||
| + | The positions of the codebook vectors are obtained with a supervised training algorithm, and you have two different ones to choose from. The default one is called Learning Vector Quantization (LVQ) and it adjusts the positions of the codebook vectors using both the correct and incorrect classified data. The second training algorithm is the competitive training algorithm, which is also used for unsupervised networks. For VQ networks this training algorithm can be used by considering the data and the codebook vectors of a specific class independently of the rest of the data and the rest of the codebook vectors. In contrast to the unsupervised networks, the output data indicating the correct class is also necessary. They are used to divide the input data among the different classes. | ||
gnd/ann.txt · Last modified: 2007/05/28 02:20 (external edit)
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