Handbook of Machine Learning

This is a comprehensive book on the theories of artificial intelligence with an emphasis on their applications. It combines fuzzy logic and neural networks, as well as hidden Markov models and genetic algorithm, describes advancements and applications of these machine learning techniques and describes the problem of causality. This book should serves as a useful reference for practitioners in artificial intelligence.
Contents:IntroductionMulti-layer PerceptronRadial Basis FunctionAutomatic Relevance DeterminationBayesian NetworksSupport Vector MachinesFuzzy LogicRough SetsHybrid MachinesAuto-associative NetworksEvolving NetworksCausalityGaussian Mixture ModelsHidden Markov ModelsReinforcement LearningConclusion Remarks
Readership: Practitioners in artificial intelligence. Artificial Intelligence;Machine Learning;Computational Intelligence;Neural Networks;Fuzzy Logic0Key Features:Comprehensive coverage of topics in computational intelligence and machine learningIt is rich in applicationsIt covers the topic of causality