Overview
- 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
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Table of contents (9 chapters)
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Introduction to Matrix Algebra
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Artificial Intelligence
Keywords
About this book
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.
Reviews
“The book is of very high relevance for students, professors and researchers involved in artificial intelligence (AI), the work is also of very high relevance for the mathematics community in general since it addresses the importance of matrix algebra for the field of AI and how major approaches and state-of-the-art algorithms rely on matrix algebra.” (Carlos Pedro Gonçalves, zbMATH 1455.68010, 2021)
Authors and Affiliations
About the author
Bibliographic Information
Book Title: A Matrix Algebra Approach to Artificial Intelligence
Authors: Xian-Da Zhang
DOI: https://doi.org/10.1007/978-981-15-2770-8
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2020
Hardcover ISBN: 978-981-15-2769-2Published: 23 May 2020
Softcover ISBN: 978-981-15-2772-2Published: 23 May 2021
eBook ISBN: 978-981-15-2770-8Published: 23 May 2020
Edition Number: 1
Number of Pages: XXXIV, 820
Number of Illustrations: 389 b/w illustrations
Topics: Artificial Intelligence, Math Applications in Computer Science, Linear and Multilinear Algebras, Matrix Theory