000			01787 a2200241 4500
005			20250926125252.0
008			241204b |||||||| |||| 00| 0 eng d
020			_a9783319639123
041			_aeng
082			_a006.3 _bKubM2
100			_aKubat, Miroslav
245			_aAn Introduction to Machine Learning [2nd ed.] / _cMiroslav Kubat
250			_a2nd ed.
260			_aSwitzerland: _bSpringer; _c©2015
300			_axiii, 348p.
520			_aThis 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.
650			_aComputer Science
650			_aArtificial Intelligence
650			_aBig Data/Analytics
650			_aData Mining
650			_aKnowledge Discovery
942			_cBK
999			_c7048 _d7048