First-order and Stochastic Optimization Me...

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Beschreibung

This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.

Zusammenfassung

Presents comprehensive study of topics in machine learning from introductory material through most complicated algorithms

Summarizes most recent findings in the area of machine learning

Addresses a broad audience in machine learning, artificial intelligence, and mathematical programming

Includes exercises

Inhaltsverzeichnis

Machine Learning Models.- Convex Optimization Theory.- Deterministic Convex Optimization.- Stochastic Convex Optimization.- Convex Finite-sum and Distributed Optimization.- Nonconvex Optimization.- Projection-free Methods.- Operator Sliding and Decentralized Optimization.

Erscheinungsjahr:	2020
Fachbereich:	Allgemeines
Genre:	Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Reihe:	Springer Series in the Data Sciences
Inhalt:	xiii 582 S. 2 s/w Illustr. 16 farbige Illustr. 582 p. 18 illus. 16 illus. in color.
ISBN-13:	9783030395674
ISBN-10:	3030395677
Sprache:	Englisch
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Autor:	Lan, Guanghui
Auflage:	1st ed. 2020
Hersteller:	Springer Nature Switzerland Springer International Publishing Springer International Publishing AG Springer Series in the Data Sciences
Maße:	241 x 160 x 38 mm
Von/Mit:	Guanghui Lan
Erscheinungsdatum:	16.05.2020
Gewicht:	1,051 kg

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