國立虎尾科技大學 |

A Matrix Algebra Approach to Artificial Intelligence

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A Matrix Algebra Approach to Artificial Intelligence/ by Xian-Da Zhang.
作者:	Zhang, Xian-Da.
面頁冊數:	XXXIV, 820 p. 389 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Linear and Multilinear Algebras, Matrix Theory. -
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. - 1st ed. 2020. - XXXIV, 820 p. 389 illus.online resource.

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:

672090
Linear and Multilinear Algebras, Matrix Theory.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

A Matrix Algebra Approach to Artificial Intelligence
LDR:02667nam a22003975i 4500 001 1027908
003 DE-He213
005 20200630130607.0
007 cr nn 008mamaa
008 210318s2020 si | s |||| 0|eng d
020 $a 9789811527708 $9 978-981-15-2770-8
024 7 $a 10.1007/978-981-15-2770-8 $2 doi
035 $a 978-981-15-2770-8
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Zhang, Xian-Da. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1324398
245 1 2 $a A Matrix Algebra Approach to Artificial Intelligence $h [electronic resource] / $c by Xian-Da Zhang.
250 $a 1st ed. 2020.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a XXXIV, 820 p. 389 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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.
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. .
650 2 4 $a Linear and Multilinear Algebras, Matrix Theory. $3 672090
650 2 4 $a Math Applications in Computer Science. $3 669887
650 1 4 $a Artificial Intelligence. $3 646849
650 0 $a Algebra. $2 gtt $3 579870
650 0 $a Matrix theory. $3 1023862
650 0 $a Computer science—Mathematics. $3 1253519
650 0 $a Artificial intelligence. $3 559380
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811527692
776 0 8 $i Printed edition: $z 9789811527715
776 0 8 $i Printed edition: $z 9789811527722
856 4 0 $u https://doi.org/10.1007/978-981-15-2770-8
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)