ULEAD

An Introduction to Machine Learning

By: Kubat, Miroslav
Series: An Introduction to Machine LearningPublisher: Estados Unidos de América: Springer, 2017Edition: 2 edDescription: 348 p. DigitalSubject(s): MACHINE-LEARNING | TECNOLOGY | COMPUTATION | CODE | ALGORITHM | DATA | IIC20DDC classification: Online resources: ►►DOWNLOAD EBOOK / DESCARGAR LIBRO DIGITAL◄◄
Contents:
REFERENCIA FORMATO APA: Kubát, M. (2017). An introduction to machine learning. Cham: Springer.
Summary: This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant 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. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
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REFERENCIA FORMATO APA: Kubát, M. (2017). An introduction to machine learning. Cham: Springer.

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant 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.

This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.

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