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An introduction to machine learning By Miroslav Kubat.

By: Material type: TextTextPublication details: Switzerland: Springer, c2021.Edition: 3Description: i-xviii+458PISBN:
  • 9783030819347
Subject(s): DDC classification:
  • 006.3 KUB-I
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.
Summary: 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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Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Computational Biology and Bioinformatics Processing Center Dept. of Computational Biology and Bioinformatics 006.3 KUB-I (Browse shelf(Opens below)) Available DCB4124

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.

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