01955nam a2200229 4500
427827
427827
ISI Library, Kolkata
20200623125623.0
200623b ||||| |||| 00| 0 eng d
9789813229686
ISI Library
English
23
006.3101515
Si 593
Simovici, Dan
author
Mathematical analysis for machine learning and data mining/ by
Dan Simovici
New Jersey:
World Scientific,
[2018]
xv, 968 pages,
23cm.
Includes bibliographical references and index
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
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.
Machine Learning-Mathematics
Data Mining-Mathematics
ddc
BK