By C. H. Wu, J. W. McLarty
This ebook is a complete reference within the box of neural networks and genome informatics. the academic of neural community foundations introduces easy neural community know-how and terminology. this is often by means of an in-depth dialogue of particular procedure designs for development neural networks for genome informatics, and huge stories and reviews of present state of the art equipment within the box. This booklet concludes with an outline of open learn difficulties and destiny examine instructions.
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Additional resources for Neural Networks and Genome Informatics
Several books are listed in the reference section with excellent descriptions of radial basis function networks and applications (Beale & Jackson, 1991; Fu, 1994; Wasserman, 1993). 2 Kohonen Self-organizing Maps Perceptrons, multilayer perceptrons and radial basis function networks require supervised training with data for which the answers are known. Some applications require the automatic clustering of data, data for which the clusters and clustering criteria are not known. One of the best known architectures for such problems is the Kohonen selforganizing map (SOM), named after its inventor, Teuvo Kohonen (Kohonen, 1997).
In this section the rationale behind such networks is described. 1 Background Network diagrams and symbols for the self-organizing map networks are quite different from those used for perceptrons, multilayer perceptrons and radial basis function networks. Input units are the same, but there are no hidden or output layers or artificial neurons. 5). Each connection between an input unit and grid unit is associated with a weight, or measure of strength (as for multilayer perceptrons). Typically, self-organizing maps are shown without the input vector.
4 Examples of different threshold values. 4. Sometimes a thresholding function is referred to as a squashing function since a large input value is squashed into a small range of output values. Another common name for the threshold value 0 is bias. The idea is that each neuron may have its own built-in bias term, independent of the input. One way of handling this pictorially and computationally is to add an extra unit to the input layer that always has a value of -1. Then the weight of the connections between this unit and the neurons in the next layer is the threshold or bias values for those neurons and the summation operation includes the bias term automatically.