03945nam 22006615 450 991057404500332120251202142448.09783030983161(electronic bk.)978303098315410.1007/978-3-030-98316-1(MiAaPQ)EBC6992172(Au-PeEL)EBL6992172(CKB)22444039900041(PPN)269152911(BIP)84243033(BIP)83221028(DE-He213)978-3-030-98316-1(EXLCZ)992244403990004120220517d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAn Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces /by Sergei Pereverzyev1st ed. 2022.Cham :Springer International Publishing :Imprint: Birkhäuser,2022.1 online resource (160 pages)Compact Textbooks in Mathematics,2296-455XPrint version: Pereverzyev, Sergei An Introduction to Artificial Intelligence Based on Reproducing Kernel Hilbert Spaces Cham : Springer International Publishing AG,c2022 9783030983154 Includes bibliographical references and index.Introduction -- Learning in Reproducing Kernel Hilbert Spaces and related integral operators -- Selected topics of the regularization theory -- Regularized learning in RKHS -- Examples of Applications.This 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.Compact Textbooks in Mathematics,2296-455XFunctional analysisOperator theoryMachine learningArtificial intelligenceFunctional AnalysisOperator TheoryMachine LearningArtificial IntelligenceFunctional analysis.Operator theory.Machine learning.Artificial intelligence.Functional Analysis.Operator Theory.Machine Learning.Artificial Intelligence.515.733Pereverzev Sergei V.1180221MiAaPQMiAaPQMiAaPQ9910574045003321An introduction to artificial intelligence based on reproducing kernel hilbert spaces2991995UNINA