Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems - Softcover

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HOJJAT ADELI received his PhD from Stanford University in 1976. He is currently Professor of Engineering and a member of the Center for Cognitive Science at Ohio State University. A contributor to 40 research and scientific journals, he has authored over 250 research and scientific publications, including four pioneering books, and edited ten books in various areas of computer science and engineering. Professor Adeli is the Editor in Chief of the journal, Integrated Computer-aided Engineering. SHIH-LIN HUNG received his MS and PhD from Ohio State University in 1990 and 1992, respectively. He is currently an Associate Professor at the National Chiao Tung University, Taiwan, Republic of China. He has authored thirteen papers in the areas of expert systems, neural networks, machine learning, and parallel processing.

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Artificial Intelligence/Neural Networks Cutting–edge approaches to designing machine learning technologies One of the most fascinating and promising developments to emerge in the field of artificial intelligence over the past decade has been machine learning. While automatic learning is still in its infancy, enormous strides have already been made toward developing expert computing systems with an impressive degree of learning capability. This book offers in–depth coverage of the latest developments in machine learning algorithms using object–oriented programming, neural nets, genetic algorithms, and fuzzy systems. While they concentrate most heavily on neural nets, the authors describe a number of ingenious new ways of integrating neural learning models with other, fundamentally different problem–solving paradigms to create powerful hybrids that can radically improve the learning and decision–making skills of computing systems.

• The only book to apply neural nets, genetic algorithms, and fuzzy systems to the field of machine learning
• Includes many specific algorithms
• Presents applications in the domains of image recognition and engineering design