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Title
A matrix algebra approach to artificial intelligence / Xian-Da Zhang.
ISBN
9789811527708 (electronic book)
9811527709 (electronic book)
9811527695
9789811527692
Published
Singapore : Springer, 2020.
Language
English
Description
1 online resource (xxxiv, 820 pages) : illustrations.
Item Number
10.1007/978-981-15-2
Call Number
QA188
Dewey Decimal Classification
512.9/434
006.3
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Available in Other Form
Print version: 9789811527692
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.
