A Matrix Algebra Approach to Artificial Intelligence

Xian-Da Zhang (Autor)

Buch | Softcover

820 Seiten

2021 | 1st ed. 2020
Springer Verlag, Singapore
978-981-15-2772-2 (ISBN)

Artikel merken

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.

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).

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.

Erscheinungsdatum	07.06.2021
Zusatzinfo	389 Illustrations, black and white; XXXIV, 820 p. 389 illus.
Verlagsort	Singapore
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Mathematik ► Algebra
	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
ISBN-10	981-15-2772-5 / 9811527725
ISBN-13	978-981-15-2772-2 / 9789811527722
Zustand	Neuware