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A Matrix Algebra Approach to Artificial Intelligence
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A Matrix Algebra Approach to Artificial Intelligence
Zhang, Xian-Da
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.
Details
Published by: Springer
Publication Date: 2020-05-23
Format: Hardcover
ISBN-13: 9789811527692
DOI: 10.1007/978-981-15-2770-8
Dimensions: 235cm x155cm
Pages: 820