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Record Nr. |
UNINA9910437580803321 |
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Autore |
Kruse Rudolf |
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Titolo |
Computational Intelligence : A Methodological Introduction / / by Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Matthias Steinbrecher, Pascal Held |
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Pubbl/distr/stampa |
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London : , : Springer London : , : Imprint : Springer, , 2013 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (488 p.) |
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Collana |
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Texts in Computer Science, , 1868-095X |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Engineering mathematics |
Engineering - Data processing |
Artificial Intelligence |
Mathematical and Computational Engineering Applications |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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Introduction -- Part I: Neural Networks -- Introduction -- Threshold Logic Units -- General Neural Networks -- Multi-Layer Perceptrons -- Radial Basis Function Networks -- Self-Organizing Maps -- Hopfield Networks -- Recurrent Networks -- Mathematical Remarks -- Part II: Evolutionary Algorithms -- Introduction to Evolutionary Algorithms -- Elements of Evolutionary Algorithms -- Fundamental Evolutionary Algorithms -- Special Applications and Techniques -- Part III: Fuzzy Systems -- Fuzzy Sets and Fuzzy Logic -- The Extension Principle -- Fuzzy Relations -- Similarity Relations -- Fuzzy Control -- Fuzzy Clustering -- Part IV: Bayes Networks -- Introduction to Bayes Networks -- Elements of Probability and Graph Theory -- Decompositions -- Evidence Propagation -- Learning Graphical Models. |
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Sommario/riassunto |
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Computational intelligence (CI) encompasses a range of nature-inspired methods that exhibit intelligent behavior in complex environments. This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successful application of CI methods, the book describes fundamental |
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concepts and their practical implementations, and explains the theoretical background underpinning proposed solutions to common problems. Only a basic knowledge of mathematics is required. Topics and features: Provides electronic supplementary material at an associated website, including module descriptions, lecture slides, exercises with solutions, and software tools Contains numerous examples and definitions throughout the text Presents self-contained discussions on artificial neural networks, evolutionary algorithms, fuzzy systems and Bayesian networks Covers the latest approaches, including ant colony optimization and probabilistic graphical models Written by a team of highly-regarded experts in CI, with extensive experience in both academia and industry Students of computer science will find the text a must-read reference for courses on artificial intelligence and intelligent systems. The book is also an ideal self-study resource for researchers and practitioners involved in all areas of CI. |
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