数学基础

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2019-08-13 DOI:10.1093/oso/9780198714934.003.0001

Max A. Little

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引用次数: 0

摘要

统计机器学习和信号处理是应用数学中的主题，它们基于许多抽象的数学概念。清晰地定义这些概念是本书最重要的第一步。本章的目的是介绍这些基本的数学概念。这也证明了这样一种说法是正确的，即统计机器学习的艺术在很大程度上应用于信号处理，在于选择在实践中碰巧有用的方便数学模型。在这种情况下，方便意味着选择数学建模假设的代数结果在某种意义上是可管理的。这种可管理性的种子是本学科赖以建立的基本数学概念。

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Mathematical foundations

Statistical machine learning and signal processing are topics in applied mathematics, which are based upon many abstract mathematical concepts. Defining these concepts clearly is the most important first step in this book. The purpose of this chapter is to introduce these foundational mathematical concepts. It also justifies the statement that much of the art of statistical machine learning as applied to signal processing, lies in the choice of convenient mathematical models that happen to be useful in practice. Convenient in this context means that the algebraic consequences of the choice of mathematical modeling assumptions are in some sense manageable. The seeds of this manageability are the elementary mathematical concepts upon which the subject is built.

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IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing

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