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    MACHINE LEARNING
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    Sinopse

    This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. 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 R, making the book as self-contained as possible.

    It starts with an introduction to Machine Learning concepts and algorithms such as the Perceptron, Multilayer Perceptron and the Distance-Weighted Nearest Neighbors with examples, in order to provide the necessary foundation so the reader is able to understand the Bias-Variance Dilemma, which is the central point of the Statistical Learning Theory.

    Afterwards, we introduce all assumptions and formalize 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, we introduce all necessary optimization concepts related to the implementation of Support Vector Machines. To provide a next stage of development, the book finishes with a discussion on SVM kernels as a way and motivation to study data spaces and improve classification results.
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    Especificações

    ISBN9783319949888
    SubtítuloA PRACTICAL APPROACH ON THE STATISTICAL LEARNING THEORY
    Pré vendaNão
    Peso721g
    Autor para link
    Livro disponível - pronta entregaNão
    Dimensões23.4 x 15.6 x 2.2
    IdiomaInglês
    Tipo itemLivro Importado
    Número de páginas380
    Número da edição1ª EDIÇÃO - 2018
    Código Interno858527
    Código de barras9783319949888
    AcabamentoHARDCOVER
    AutorMELLO, RODRIGO FERNNDES DE | PONTI, MOACIR ANTONELLI
    EditoraSPRINGER
    Sob encomendaSim

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