LEADER 00758nas 22002652a 450 001 9910402050903321 005 20240413012549.0 035 $a(CKB)991042754056054 035 $a(CONSER)sn-98001014- 035 $a(EXLCZ)99991042754056054 100 $a19980706a19989999 --- - 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aCountry review$iMali 210 $aHouston, TX $cCommercial Data International 215 $a1 online resource 311 08$aPrint version: Country review. 1520-0868 (DLC)sn-98001014- (OCoLC)39396098 517 3 $aMali 906 $aJOURNAL 912 $a9910402050903321 920 $aexl_impl conversion 996 $aCountry review$92425273 997 $aUNINA LEADER 03358nam 22005655 450 001 9910409667903321 005 20211020193544.0 010 $a981-15-2770-9 024 7 $a10.1007/978-981-15-2770-8 035 $a(CKB)4100000011264519 035 $a(MiAaPQ)EBC6208491 035 $a(DE-He213)978-981-15-2770-8 035 $a(PPN)248393367 035 $a(EXLCZ)994100000011264519 100 $a20200522d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA Matrix Algebra Approach to Artificial Intelligence /$fby Xian-Da Zhang 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (xxxiv, 820 pages) 311 $a981-15-2769-5 327 $aPart 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. 330 $aMatrix 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. . 606 $aArtificial intelligence 606 $aComputer science$xMathematics 606 $aMatrices 606 $aAlgebra 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aMath Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17044 606 $aLinear and Multilinear Algebras, Matrix Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/M11094 615 0$aArtificial intelligence. 615 0$aComputer science$xMathematics. 615 0$aMatrices. 615 0$aAlgebra. 615 14$aArtificial Intelligence. 615 24$aMath Applications in Computer Science. 615 24$aLinear and Multilinear Algebras, Matrix Theory. 676 $a006.3 700 $aZhang$b Xian-Da$4aut$4http://id.loc.gov/vocabulary/relators/aut$0968603 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910409667903321 996 $aA Matrix Algebra Approach to Artificial Intelligence$92200126 997 $aUNINA LEADER 01173nas 2200421- 450 001 9910894166003321 005 20220315131545.0 035 $a(OCoLC)853476262 035 $a(CKB)2670000000345053 035 $a(CONSER)--2014238581 035 $a(DE-599)ZDB2769928-6 035 $a(EXLCZ)992670000000345053 100 $a20130722a20129999 --- a 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aInternational journal of bioassays 210 1$a[India] :$c[E-Bioscholar] 215 $a1 online resource 311 08$a2278-778X 517 1 $aInt. j. bioassays 517 1 $aIJB 517 1 $aIJBIO 606 $aBiology$vPeriodicals 606 $aBiological assay$vPeriodicals 606 $aBiological assay$2fast$3(OCoLC)fst00832194 606 $aBiology$2fast$3(OCoLC)fst00832383 608 $aPeriodicals.$2fast 615 0$aBiology 615 0$aBiological assay 615 7$aBiological assay. 615 7$aBiology. 906 $aJOURNAL 912 $a9910894166003321 996 $aInternational journal of bioassays$94262805 997 $aUNINA