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Schölkopf, Bernhard: Learning with Kernels : support vector machines, regularization, optimization, and beyond / Bernhard Schölkopf, Alexander J. Smola. -
Cambridge, Massachusetts: MIT Press, [2002]. - 1 Online-Ressource (XVIII, 626 Seiten) : Illustrationen, ISBN 978-0-262-25693-3
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Ernst, Floris: Fundamentals of machine learning : support vector machines made easy / Floris Ernst, Achim Schweikard. - 1. Auflage. -
München: UVK Verlag, 2020. - 154 Seiten : Diagramme, ISBN 978-3-8252-5251-9
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Signatur: LN-U 10-19353
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Aggarwal, Charu C.: Neural Networks and Deep Learning : A Textbook / by Charu C. Aggarwal. - 2nd ed. 2023.. -
Cham: Springer International Publishing, 2023.. - 1 Online-Ressource(XXIV, 529 p. 150 illus., 22 illus. in color.), ISBN 978-3-031-29642-0
DOI: 10.1007/978-3-031-29642-0
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Murty, M.N.: Support Vector Machines and Perceptrons : Learning, Optimization, Classification, and Application to Social Networks / by M.N. Murty, Rashmi Raghava. -
Cham: Springer, 2016. - Online-Ressource (XIII, 95 p. 25 illus, online resource), ISBN 978-3-319-41063-0
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DOI: 10.1007/978-3-319-41063-0
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Wegner, Sven-Ake: Mathematische Einführung in Data Science / von Sven-Ake Wegner. - 1st ed. 2023.. -
Berlin, Heidelberg: Springer Berlin Heidelberg, 2023.. - 1 Online-Ressource(IX, 303 S. 118 Abb.), ISBN 978-3-662-68697-3
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Johannes, Marc: Integration of prior biological knowledge into Support Vector Machines / Marc Johannes, 2011. - Online-Ressource
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International Conference on Computer Science and Mechanical Automation <2., 2016, Wuhan>: Current trends in computer science and mechanical automation : selected papers from CSMA2016 - Volume 1 / Shawn X. Wang (ed.). -
Warsaw ; Berlin: De Gruyter Open, 2018. - 1 Online-Ressource (XX, 637 Seiten), ISBN 978-3-11-058497-4
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Murphy, Kevin P.: Probabilistic machine learning : advanced topics / Kevin P. Murphy. -
Cambridge, Massachusetts ; London, England: The MIT Press, [2023]. - xxxi, 1319 Seiten : Illustrationen, Diagramme, ISBN 978-0-262-04843-9
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James, Gareth: An introduction to statistical learning : with applications in R / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. - Second edition. -
New York, NY: Springer, 2021. - 1 Online-Ressource (xv, 607 Seiten) : Illustrationen, Diagramme, ISBN 978-1-0716-1418-1
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Machine Learning under Resource Constraints : Applications / edited by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer. -
Berlin ; Boston: De Gruyter, 2022. - 1 Online-Ressource (VIII, 470 Seiten), ISBN 978-3-11-078598-2
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(De Gruyter STEM)
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Johannes, Marc: Integration of prior biological knowledge into Support Vector Machines / presented by Marc Johannes, 2011. - 100 S. : Ill., graph. Darst.
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Neuhaus, Michel: Bridging the gap between graph edit distance and Kernel machines / Michel Neuhaus; Horst Bunke. -
Singapore [u.a.]: World Scientific, 2007. - XI, 232 S. : Ill., graf. Darst., ISBN 978-981-270-817-5
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Optimization and machine learning : optimization for machine learning and machine learning for optimization / coordinated by Rachid Chelouah, Patrick Siarry. -
London, UK ; Hoboken, NJ: Wiley, 2022. - 1 online resource., ISBN 978-1-119-90288-1
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Cristianini, Nello: An introduction to support vector machines and other kernel-based learning methods / Nello Cristianini and John Shawe-Taylor. - Reprinted (with corrections). -
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James, Gareth: An introduction to statistical learning : with applications in R / Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. - Corrected at 8th printing. -
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(Springer texts in statistics)
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Murphy, Kevin P.: Probabilistic machine learning : an introduction / Kevin P. Murphy. -
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Heitzinger, Clemens: Algorithms with JULIA : Optimization, Machine Learning, and Differential Equations Using the JULIA Language / by Clemens Heitzinger. - 1st ed. 2022.. -
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Machine Learning under Resource Constraints : Discovery in Physics / edited by Katharina Morik, Wolfgang Rhode. -
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Alpaydın, Ethem: Introduction to machine learning / Ethem Alpaydin. - Fourth edition. -
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Cristianini, Nello: An introduction to support vector machines and other kernel-based learning methods / Nello Cristianini and John Shawe-Taylor. - 10. print.. -
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