Mathematical Analysis for Machine Learning and Data Mining (eBook)
by dan simovici (Author)

 984 Pages
<! <description> >
This compendium provides a selfcontained 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.
<! </description> >Contents: SetTheoretical and Algebraic Preliminaries:
 Preliminaries
 Linear Spaces
 Algebra of Convex Sets
 Topology:
 Topology
 Metric Space Topologies
 Topological Linear Spaces
 Measure and Integration:
 Measurable Spaces and Measures
 Integration
 Functional Analysis and Convexity:
 Banach Spaces
 Differentiability of Functions Defined on Normed Spaces
 Hilbert Spaces li>Convex Functions
 Applications:
 Optimization
 Iterative Algorithms
 Neural Networks
 Regression
 Support Vector Machines
<! <readership> >Readership: Researchers, academics, professionals and graduate students in artificial intelligence, and mathematical modeling.<! </readership> >
Measure Theory;Banach Spaces;Hilbert Spaces;Convexity;Support Vector Machines;Neural Networks;Regression;Optimization00
 Released: May 21, 2018
 Categories: Computers & Internet
 Language: English
 Publisher: World Scientific Publishing Company
 ISBN10: 9813229705
 ISBN13: 9789813229709