Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models by Vojislav Kecman

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models Vojislav Kecman ebook
Format: pdf
Publisher: The MIT Press
ISBN: 0262112558, 9780262112550
Page: 576

(a) A Mamdani-type FIS and (b) a fuzzy inference system as neural network. The inference part handles the resulting values and The basic of fuzzy rules is the binary logic (IF . The fuzzifier processes the inputs according to the membership function for the inputs. To make this model selection procedure convenient for clinical use, a learning technique based on neuro-fuzzy systems originally proposed for intelligence control was used for the current study. This carefully edited monograph presents Incorporating probabilistic support vector machine and active learning, Chua and Feng present a bootstrapping framework for annotating the semantic concepts of large collections of images. Currently, Genetic Algorithms is used along with neural networks and fuzzy logic for solving more complex problems. Because of their joint generic name: “;soft-computing”. A Genetic evaluated with the help of some functions, representing the constraints of the problem. Models, called Genetic Algorithms (GA), that mimic the biological evolution process for search, optimization and machine learning. The past years have witnessed a large number of interesting applications of various soft computing techniques, such as fuzzy logic, neural networks, and evolutionary computation, to intelligent multimedia processing.