A Matrix Algebra Approach to Artificial Intelligence

Xian-Da Zhang

ISBN 10: 9811527695 / ISBN 13: 9789811527692

Published by Springer Nature Singapore, Springer Nature Singapore, 2020

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9789811527692

Bibliographic Details

• Title: A Matrix Algebra Approach to Artificial Intelligence
• Publisher: Springer Nature Singapore, Springer Nature Singapore
• Publication Date: 2020
• Language: English
• ISBN 10: 9811527695
• ISBN 13: 9789811527692
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 48D millimeters 241 millimeters width by 160 millimeters height by 48 millimeters depth
• Weight: 1587 grams

About this title

Synopsis

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.  

About the Author

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).