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An introduction to machine learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	An introduction to machine learning/ by Miroslav Kubat.
作者:	Kubat, Miroslav.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	xiii, 291 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Machine learning. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-20010-1
ISBN:	9783319200101 (electronic bk.)

An introduction to machine learning
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:

561253
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3

An introduction to machine learning
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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.
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650 2 4 $a Simulation and Modeling. $3 669249
650 2 4 $a Information Storage and Retrieval. $3 593926
650 2 4 $a Pattern Recognition. $3 669796
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