A Matrix Algebra Approach to Artificial In...

Dekorationsartikel gehören nicht zum Leistungsumfang.

Sprache: Englisch

214,95 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 1-2 Wochen

Kategorien:

Der Artikel ist nicht in deutscher Sprache verfasst.
Die Sprache der Produktbeschreibung und Details kann in Englisch angegeben sein. Sollten Sie Verständnisprobleme haben, wenden Sie sich gerne an uns.

Beschreibung

Matrix algebra plays an important role in many core artificial intelligence (AI) areas, including machine learning, neural networks, support vector machines (SVMs) and evolutionary computation. This book offers a comprehensive and in-depth discussion of matrix algebra theory and methods for these four core areas of AI, while also approaching AI from a theoretical matrix algebra perspective.

The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines, including, but not limited to, computer science, mathematics and engineering.

Über den Autor

XIAN-DA ZHANG is a Professor Emeritus at the Department of Automation, Tsinghua University, China. He was a Distinguished Professor at Xidian University, Xi'an, China, as part of the Ministry of Education of China and Cheung Kong Scholars Programme, from 1999 to 2002. His areas of research include intelligent signal and information processing, pattern recognition, machine learning and neural networks, evolutional computation, and correlated applied mathematics. He has published over 120 international journal and conference papers. The Japanese translation of his book "Linear Algebra in Signal Processing" (published in Chinese by Science Press, Beijing, in 1997) was published by Morikita Press, Tokyo, in 2008. He also authored the book "Matrix Analysis and Applications" (Cambridge University Press, UK, 2017).

Zusammenfassung

Proposes the machine learning tree, the neural network tree and the evolutionary computation tree

Presents the solid matrix algebra theory and methods for machine learning, neural networks, support vector machines and evolutionary computation

Highlights selected topics and advances in machine learning, neural networks and evolutionary computation

Summarizes about 80 AI algorithms so that readers can further understand and implement relevant AI methods

Inhaltsverzeichnis

Part 1. Introduction to Matrix Algebra.- Chapter 1. Basic Matrix Computation.- Chapter 2. Matrix Differential.- Chapter 3. Gradient and Optimization.- Chapter 4. Solution of Linear Systems.- Chapter 5. Eigenvalue Decomposition.- Part 2. Artificial Intelligence.- Chapter 6. Machine Learning.- Chapter 7. Neural Networks.- Chapter 8. Support Vector Machines.- Chapter 9. Evolutionary Computation.

Details

Erscheinungsjahr:	2020
Genre:	Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Inhalt:	xxxiv 820 S. 389 s/w Illustr. 820 p. 389 illus.
ISBN-13:	9789811527692
ISBN-10:	9811527695
Sprache:	Englisch
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Autor:	Zhang, Xian-Da
Auflage:	1st ed. 2020
Hersteller:	Springer Singapore Springer Nature Singapore
Maße:	241 x 160 x 48 mm
Von/Mit:	Xian-Da Zhang
Erscheinungsdatum:	23.05.2020
Gewicht:	1,587 kg

Artikel-ID: 117883260