By Miroslav Kubat
This textbook offers basic desktop studying ideas in a simple to appreciate demeanour through delivering useful recommendation, utilizing user-friendly examples, and supplying enticing discussions of appropriate purposes. the most subject matters comprise Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, choice bushes, neural networks, and help vector machines. Later chapters express how one can mix those basic instruments when it comes to “boosting,” find out how to make the most them in additional advanced domain names, and the way to accommodate different complex useful matters. One bankruptcy is devoted to the preferred genetic algorithms.
This revised variation comprises 3 solely new chapters on serious themes concerning the pragmatic software of desktop studying in undefined. The chapters study multi-label domain names, unsupervised studying and its use in deep studying, and logical methods to induction. quite a few chapters were elevated, and the presentation of the cloth has been superior. The booklet includes many new routines, a number of solved examples, thought-provoking experiments, and machine assignments for self sustaining work.
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Additional resources for An Introduction to Machine Learning
An Introduction to Machine Learning by Miroslav Kubat