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020 _a9783319639130
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024 7 _a10.1007/978-3-319-63913-0
_2doi
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082 0 4 _a006.312
_223
100 1 _aKubat, Miroslav.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 3 _aAn Introduction to Machine Learning
_h[electronic resource] /
_cby Miroslav Kubat.
250 _a2nd ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXIII, 348 p. 85 illus., 3 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1 A Simple Machine-Learning Task -- 2 Probabilities: Bayesian Classifiers -- Similarities: Nearest-Neighbor Classifiers -- 4 Inter-Class Boundaries: Linear and Polynomial Classifiers -- 5 Artificial Neural Networks -- 6 Decision Trees -- 7 Computational Learning Theory -- 8 A Few Instructive Applications -- 9 Induction of Voting Assemblies -- 10 Some Practical Aspects to Know About -- 11 Performance Evaluation -- 12 Statistical Significance -- 13 Induction in Multi-Label Domains -- 14 Unsupervised Learning -- 15 Classifiers in the Form of Rulesets -- 16 The Genetic Algorithm -- 17 Reinforcement Learning.
520 _aThis textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aBig data.
650 0 _aComputational intelligence.
650 1 4 _aData Mining and Knowledge Discovery.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I18030
650 2 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650 2 4 _aBig Data/Analytics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/522070
650 2 4 _aComputational Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T11014
856 4 0 _uhttps://doi.org/10.1007/978-3-319-63913-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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