01876nam a2200205 4500003002100000005001700021008004100038020001800079040002500097082002800122100002600150245008200176260004400258300002600302504005000328505058600378520064500964650003301609650002801642ISI Library, Kolkata20200623125623.0200623b ||||| |||| 00| 0 eng d a9789813229686 aISI LibrarybEnglish04223a006.3101515bSi 593 aSimovici, Daneauthor10aMathematical analysis for machine learning and data mining/ by cDan Simovici aNew Jersey:bWorld Scientific, c[2018] axv, 968 pages,c23cm. aIncludes bibliographical references and index0 aPreface,
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 aThis 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. 4aMachine Learning-Mathematics 4aData Mining-Mathematics