Titlebook: Machine Learning; A Practical Approach Rodrigo Fernandes de Mello,Moacir Antonelli Ponti Textbook 2018 Springer International Publishing AG

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Textbook 2018. It can be used as a textbook in graduation or undergraduation courses, for self-learners, or as reference with respect to the main theoretical concepts of Machine Learning. Fundamental concepts of Linear Algebra and Optimization applied to Machine Learning are provided, as well as source codes in

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A Brief Introduction on Kernels,ace is sufficiently linearly separable. On the other hand, many input spaces are, in fact, not linearly separable. In order to overcome this restriction, nonlinear transformations can be used to implicitly obtain a more adequate space.

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Textbook 2018alize the Statistical Learning Theory, allowing the practical study of different classification algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. . From that,

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Rodrigo Fernandes de Mello,Moacir Antonelli Pontierkmale.viele Tipps und Checklisten für die tägliche Routine.Neu in der 7. Auflage: .Erneuerung vieler Einstellungs- und Röntgenaufnahmen an modernsten Geräten..ausführliche Darstellung digitaler Techniken in d978-3-662-56256-7

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Rodrigo Fernandes de Mello,Moacir Antonelli Pontierkmale.viele Tipps und Checklisten für die tägliche Routine.Neu in der 7. Auflage: .Erneuerung vieler Einstellungs- und Röntgenaufnahmen an modernsten Geräten..ausführliche Darstellung digitaler Techniken in d978-3-662-56256-7

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ation algorithms. Then, we proceed with concentration inequalities until arriving to the Generalization and the Large-Margin bounds, providing the main motivations for the Support Vector Machines. . From that, 978-3-030-06949-0978-3-319-94989-5

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A Brief Review on Machine Learning,osis of people under severe diseases, classify wine types, separate some material according to its quality (e.g. wood could be separated according to its weakness, so it could be later used to build either pencils or houses).

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Statistical Learning Theory,zation (ERM) principle, which is the key point for the Statistical Learning Theory (SLT). The ERM principle provides upper bounds to make the empirical risk a good estimator for the expected risk, given the bias of some learning algorithm. This bound is the main theoretical tool to provide learning

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Introduction to Support Vector Machines,vides an intuitive and an algebraic formulation to obtain the optimization problem of the Support Vector Machines. At last, hard-margin and soft-margin SVMs are detailed, including the necessary mathematical tools to tackle them both.