大葉大學圖書館 |

An introduction to machine learning[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.3
書名/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.
標題:	Computer Science.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Simulation and Modeling.
標題:	Information Storage and Retrieval.
標題:	Pattern Recognition.
ISBN:	9783319200101 (electronic bk.)
ISBN:	9783319200095 (paper)
內容註:	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.
摘要、提要註:	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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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 Information Storage and Retrieval. $3 464540
650 2 4 $a Pattern Recognition. $3 463916
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