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Overview: The comprehensive compendium furnishes a quick and efficient entry point to many multiresolution techniques and facilitates the transition from an idea into a real project. It focuses on methods combining several soft computing techniques (fuzzy logic, neural networks, genetic algorithms) in a multiresolution framework. Illustrated with numerous vivid examples, this useful volume gives the reader the necessary theoretical background to decide which methods suit his/her needs. New materials and applications for multiresolution analysis are added, including notable research topics such as Deep Learning, graphs, and network analysis. Many Deep Learning applications incorporate a wavelet decomposition stage to better capture features at different resolutions, a quite sensible step as the size of an object in an image may greatly vary. A fascinating aspect that we discuss in a new chapter is that multiresolution is at the heart of the functioning of Deep Learning. Neural networks on graphs are important in studying communication networks and analyzing internet data. Here also, multiresolution permits a better analysis. The research community has broadly integrated the idea that the integration of multiresolution often improves algorithms. This new edition aims to capture some of these exciting new developments. Readership: Researchers, professionals, academics and graduate students in fuzzy logic.
Genre: Non-Fiction > Tech & Devices
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