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An Introduction to Machine Learning / Miroslav Kubat.

By: Kubat, Miroslav, 1958-
Contributor(s): SpringerLink (Online service)
Material type: TextTextPublisher: Cham : Springer, c.2015Description: xiii, 291 p. : ill. ; 24 cmISBN: 9783319200095Subject(s): Machine learning | Informatics and Computer Science May2016Additional physical formats: Printed edition:: No titleDDC classification: 006.3
Contents:
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.
In: Springer eBooksSummary: 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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Book - Borrowing Book - Borrowing Central Library
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Alahram 006.3 KUB (Browse shelf) Available 000032448
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Index : p. 291.

Bibliography : p. 287-290.

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.

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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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