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A matrix algebra approach to artific...

Zhang, Xian-Da.

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A matrix algebra approach to artificial intelligence

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A matrix algebra approach to artificial intelligence/ by Xian-Da Zhang.
作者:	Zhang, Xian-Da.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xxxiv, 820 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Matrices. -
電子資源:	https://doi.org/10.1007/978-981-15-2770-8
ISBN:	9789811527708

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

A matrix algebra approach to artificial intelligence[electronic resource] /by Xian-Da Zhang. - Singapore :Springer Singapore :2020. - xxxiv, 820 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811527708

Standard No.: 10.1007/978-981-15-2770-8doiSubjects--Topical Terms:

516894
Matrices.

LC Class. No.: QA188 / .Z436 2020

Dewey Class. No.: 512.9434

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520 $a 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.
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