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RT Book, Whole SR Electronic DC OPAC T1 Lectures on Intelligent Systems / by Leonardo Vanneschi, Sara Silva T2 Natural Computing Series. ISSN:26276461 A1 Vanneschi, Leonardo A1 Silva, Sara A1 SpringerLink (Online service) YR 2023 FD 2023 SP XIV, 349 p. 89 illus., 36 illus. in color K1 Artificial intelligence K1 Artificial Intelligence ED 1st ed. 2023. PB Springer International Publishing : Imprint: Springer PP Cham SN 9783031179228 LA English (英語) CL LCC:Q334-342 CL LCC:TA347.A78 CL DC23:006.3 NO Chapter 1: Introduction -- Chapter 2: Optimization Problems and Local Search -- Chapter 3: Genetic Algorithms -- Chapter 4: Particle Swarm Optimization -- Chapter 5: Introduction to Machine Learning -- Chapter 6: Decision Tree Learning -- Chapter 7: Artificial Neural Networks -- Chapter 8: Genetic Programming -- Bayesian Learning -- Chapter 10: Support Vector Machines -- Chapter 11: Ensemble Methods -- Chapter 12: Unsupervised Learning NO This textbook provides the reader with an essential understanding of computational methods for intelligent systems. These are defined as systems that can solve problems autonomously, in particular problems where algorithmic solutions are inconceivable for humans or not practically executable by computers. Despite the rapidly growing applications in this field, the book avoids application details, instead focusing on computational methods that equip the reader with the methodological tools and competencies necessary to tackle current and future complex applications. The book consists of two parts: computational intelligence methods for optimization, and machine learning. Part I begins with the concept of optimization, and introduces local search algorithms, genetic algorithms, and particle swarm optimization. Part II begins with an introduction to machine learning and covers several methods, many of which can be used as supervised learning algorithms, such as decision tree learning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods, plus a discussion of unsupervised learning. This textbook is written in a self-contained style, suitable for undergraduate or graduate students in computer science and engineering, and for self-study by researchers and practitioners NO HTTP:URL=https://doi.org/10.1007/978-3-031-17922-8 NO 書誌ID=EB00001305; LK [E Book]https://doi.org/10.1007/978-3-031-17922-8 OL 30