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010   $a9783030983161$b(electronic bk.)
010   $z9783030983154
024 7 $a10.1007/978-3-030-98316-1
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035   $a(Au-PeEL)EBL6992172
035   $a(CKB)22444039900041
035   $a(PPN)269152911
035   $a(BIP)84243033
035   $a(BIP)83221028
035   $a(DE-He213)978-3-030-98316-1
035   $a(EXLCZ)9922444039900041
100   $a20220517d2022      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 13$aAn Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces /$fby Sergei Pereverzyev
205   $a1st ed. 2022.
210  1$aCham :$cSpringer International Publishing :$cImprint: Birkhäuser,$d2022.
215   $a1 online resource (160 pages)
225 1 $aCompact Textbooks in Mathematics,$x2296-455X
311 08$aPrint version: Pereverzyev, Sergei An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces Cham : Springer International Publishing AG,c2022 9783030983154 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- Learning in Reproducing Kernel Hilbert Spaces and related integral operators -- Selected topics of the regularization theory -- Regularized learning in RKHS -- Examples of Applications.
330   $aThis textbook provides an in-depth exploration of statistical learning with reproducing kernels, an active area of research that can shed light on trends associated with deep neural networks. The author demonstrates how the concept of reproducing kernel Hilbert Spaces (RKHS), accompanied with tools from regularization theory, can be effectively used in the design and justification of kernel learning algorithms, which can address problems in several areas of artificial intelligence. Also provided is a detailed description of two biomedical applications of the considered algorithms, demonstrating how close the theory is to being practically implemented. Among the book?s several unique features is its analysis of a large class of algorithms of the Learning Theory that essentially comprise every linear regularization scheme, including Tikhonov regularization as a specific case. It also provides a methodology for analyzing not only different supervised learningproblems, such as regression or ranking, but also different learning scenarios, such as unsupervised domain adaptation or reinforcement learning. By analyzing these topics using the same theoretical framework, rather than approaching them separately, their presentation is streamlined and made more approachable. An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces is an ideal resource for graduate and postgraduate courses in computational mathematics and data science.
410  0$aCompact Textbooks in Mathematics,$x2296-455X
606   $aFunctional analysis
606   $aOperator theory
606   $aMachine learning
606   $aArtificial intelligence
606   $aFunctional Analysis
606   $aOperator Theory
606   $aMachine Learning
606   $aArtificial Intelligence
615  0$aFunctional analysis.
615  0$aOperator theory.
615  0$aMachine learning.
615  0$aArtificial intelligence.
615 14$aFunctional Analysis.
615 24$aOperator Theory.
615 24$aMachine Learning.
615 24$aArtificial Intelligence.
676   $a515.733
700   $aPereverzev$b Sergei V.$01180221
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910574045003321
996   $aAn introduction to artificial intelligence based on reproducing kernel hilbert spaces$92991995
997   $aUNINA