000 | 03611nam a22005175i 4500 | ||
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001 | 978-3-031-17922-8 | ||
003 | DE-He213 | ||
005 | 20240423125300.0 | ||
007 | cr nn 008mamaa | ||
008 | 230113s2023 sz | s |||| 0|eng d | ||
020 |
_a9783031179228 _9978-3-031-17922-8 |
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024 | 7 |
_a10.1007/978-3-031-17922-8 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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_aCOM004000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aVanneschi, Leonardo. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aLectures on Intelligent Systems _h[electronic resource] / _cby Leonardo Vanneschi, Sara Silva. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2023. |
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300 |
_aXIV, 349 p. 89 illus., 36 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aNatural Computing Series, _x2627-6461 |
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505 | 0 | _aChapter 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. | |
520 | _aThis 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. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aArtificial Intelligence. |
700 | 1 |
_aSilva, Sara. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031179211 |
776 | 0 | 8 |
_iPrinted edition: _z9783031179235 |
776 | 0 | 8 |
_iPrinted edition: _z9783031179242 |
830 | 0 |
_aNatural Computing Series, _x2627-6461 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-17922-8 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
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_c176239 _d176239 |