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An introduction to machine learning[...

Kubat, Miroslav.

An introduction to machine learning[electronic resource] /

レコード種別:	言語・文字資料 (印刷物) : 単行資料
[NT 15000414] null:	006.3
タイトル / 著者:	An introduction to machine learning/ by Miroslav Kubat.
著者:	Kubat, Miroslav.
出版された:	Cham : : Springer International Publishing :, 2015.
記述:	xiii, 291 p. : : ill. (some col.), digital ;; 24 cm.
含まれています:	Springer eBooks
主題:	Machine learning.
主題:	Computer Science.
主題:	Artificial Intelligence (incl. Robotics)
主題:	Simulation and Modeling.
主題:	Information Storage and Retrieval.
主題:	Pattern Recognition.
国際標準図書番号 (ISBN) :	9783319200101 (electronic bk.)
国際標準図書番号 (ISBN) :	9783319200095 (paper)
[NT 15000228] null:	A Simple Machine-Learning Task -- Probabilities: Bayesian Classifiers -- Similarities: Nearest-Neighbor Classifiers -- Inter-Class Boundaries: Linear and Polynomial Classifiers -- Artificial Neural Networks -- Decision Trees -- Computational Learning Theory -- A Few Instructive Applications -- Induction of Voting Assemblies -- Some Practical Aspects to Know About -- Performance Evaluation -- Statistical Significance -- The Genetic Algorithm -- Reinforcement learning.
[NT 15000229] null:	This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting 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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-20010-1

An introduction to machine learning[electronic resource] /
Kubat, Miroslav.

An introduction to machine learning[electronic resource] /by Miroslav Kubat. - Cham :Springer International Publishing :2015. - xiii, 291 p. :ill. (some col.), digital ;24 cm.

A Simple Machine-Learning Task -- Probabilities: Bayesian Classifiers -- Similarities: Nearest-Neighbor Classifiers -- Inter-Class Boundaries: Linear and Polynomial Classifiers -- Artificial Neural Networks -- Decision Trees -- Computational Learning Theory -- A Few Instructive Applications -- Induction of Voting Assemblies -- Some Practical Aspects to Know About -- Performance Evaluation -- Statistical Significance -- The Genetic Algorithm -- Reinforcement learning.

This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting 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.

ISBN: 9783319200101 (electronic bk.)

Standard No.: 10.1007/978-3-319-20010-1doiSubjects--Topical Terms:

202931
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3

An introduction to machine learning[electronic resource] /
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020 $a 9783319200101 (electronic bk.)
020 $a 9783319200095 (paper)
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100 1 $a Kubat, Miroslav. $3 633173
245 1 3 $a An introduction to machine learning $h [electronic resource] / $c by Miroslav Kubat.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xiii, 291 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a A Simple Machine-Learning Task -- Probabilities: Bayesian Classifiers -- Similarities: Nearest-Neighbor Classifiers -- Inter-Class Boundaries: Linear and Polynomial Classifiers -- Artificial Neural Networks -- Decision Trees -- Computational Learning Theory -- A Few Instructive Applications -- Induction of Voting Assemblies -- Some Practical Aspects to Know About -- Performance Evaluation -- Statistical Significance -- The Genetic Algorithm -- Reinforcement learning.
520 $a This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting 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.
650 0 $a Machine learning. $3 202931
650 1 4 $a Computer Science. $3 423143
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 463642
650 2 4 $a Simulation and Modeling. $3 463796
650 2 4 $a Information Storage and Retrieval. $3 464540
650 2 4 $a Pattern Recognition. $3 463916
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-20010-1
950 $a Computer Science (Springer-11645)

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http://dx.doi.org/10.1007/978-3-319-20010-1

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