Mathematical Analysis for Machine Learning and Data Mining
Simovici, Dan
creator
author
Singapore
World Scientific
[2018]
eng
xv, 968 pages, 23cm.
This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining. The mathematical analysis component of the typical mathematical curriculum for computer science students omits these very important ideas and techniques which are indispensable for approaching specialized area of machine learning centered around optimization such as support vector machines, neural networks, various types of regression, feature selection, and clustering. The book is of special interest to researchers and graduate students who will benefit from these application areas discussed in the book.
Preface --
Part I. Set-Theoretical and Algebraic Preliminaries --
Preliminaries --
Linear Spaces --
Algebra of Convex Sets --
Part II. Topology --
Topology --
Metric Space Topologies --
Topological Linear Spaces --
Part III. Measure and Integration --
Measurable Spaces and Measures --
Integration --
Part IV. Functional Analysis and Convexity --
Banach Spaces --
Differentiability of Functions Defined on Normed Spaces --
Hilbert Spaces --
Convex Functions --
Part V. Applications --
Optimization --
Iterative Algorithms --
Neural Networks --
Regression --
Support Vector Machines --
Bibliography --
Index.
Dan Simovici
Includes bibliographical references and index
Machine Learning-Mathematics
Data Mining-Mathematics
006.3101515 Si593
9789813229686
ISI Library
200623
20200731141427.0
English