Machine Learning: A Practical Approach on the Statistical Learning Theory

Rodrigo Fernandes de Mello, Moacir Antonelli Ponti

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商品描述

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.   

商品描述(中文翻譯)

這本書以詳細且易於理解的方式介紹了統計學習理論,並使用實際例子、演算法和原始碼來說明。它可以作為研究生或本科課程的教科書,供自學者使用,或作為機器學習主要理論概念的參考資料。書中提供了線性代數和優化在機器學習中的基本概念,以及R語言的原始碼,使書籍盡可能自成一體。

書籍以機器學習概念和算法的介紹開始,例如感知器、多層感知器和加權最近鄰居法,並提供相應的例子,以便讀者能夠理解偏差-方差困境,這是統計學習理論的核心。

之後,我們介紹了所有假設並形式化了統計學習理論,從而實際研究不同分類算法。然後,我們通過集中不等式進一步探討泛化和大邊界界限,為支持向量機提供主要動機。

在此基礎上,我們介紹了與實現支持向量機相關的所有必要優化概念。為了提供下一階段的發展,書籍最後討論了SVM核函數作為研究數據空間和改善分類結果的方法和動機。