A matrix algebra approach to artificial intelligence / Xian-Da Zhang.

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A matrix algebra approach to artificial intelligence / Xian-Da Zhang.

9789811527708 (electronic book)
9811527709 (electronic book)
9811527695
9789811527692

Singapore : Springer, 2020.

English

1 online resource (xxxiv, 820 pages) : illustrations.

10.1007/978-981-15-2

QA188

512.9/434
006.3

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.

Includes bibliographical references and index.

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Print version: 9789811527692

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