A Matrix Algebra Approach to Artificial Intelligence

A Matrix Algebra Approach to Artificial Intelligence

1st Edition 2020

Paperback (23 May 2021)

  • $282.93
Add to basket

Includes delivery to the United States

10+ copies available online - Usually dispatched within 7 days

Publisher's Synopsis

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.  

Book information

ISBN: 9789811527722
Publisher: Springer Nature Singapore
Imprint: Springer
Pub date:
Edition: 1st Edition 2020
Language: English
Number of pages: 820
Weight: 1438g
Height: 235mm
Width: 155mm
Spine width: 28mm