Documentation /  Neural Networks /  Vector Quantization /

Further ReadingIntroduction

11 Vector Quantization

Vector quantization (VQ) networks are intended to be used for 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 assigned to 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. In contrast to unsupervised nets, VQ networks are trained with supervised training algorithms. This means that you need to supply output data indicating the correct class of any particular input vector during the training.

Section 2.8, Unsupervised and Vector Quantization (VQ) Networks, gives a short tutorial on VQ networks. Section 11.1, Vector Quantization Network Functions and Options, describes the functions, and their options and examples are given in Section 11.2, Examples. Section 11.3, Change Step Length, describes how you can change the training algorithm by changing the step length.

Further ReadingIntroduction


Any questions about topics on this page? Click here to get an individual response.Buy NowMore Information