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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 DOI: 10.1007/978-3-662-68697-3
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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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Blaschzyk, Ingrid Karin: Improved classification rates for localized algorithms under margin conditions / Ingrid Karin Blaschzyk. - Wiesbaden ; [Heidelberg]: Springer Spektrum, [2020]. - 1 Online-Ressource (XV, 126 Seiten) : Illustrationen, ISBN 978-3-658-29591-2 (Springer eBook Collection) (Research) DOI: 10.1007/978-3-658-29591-2
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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 (Springer texts in statistics) (Springer eBook Collection) DOI: 10.1007/978-1-0716-1418-1
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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 (SpringerLink : Bücher) (SpringerBriefs in Computer Science) (Springer eBook Collection : Computer Science) DOI: 10.1007/978-3-319-41063-0
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Blaschzyk, Ingrid Karin: Improved classification rates for localized algorithms under margin conditions / Ingrid Karin Blaschzyk. - Wiesbaden ; [Heidelberg]: Springer Spektrum, [2020]. - 126 Seiten : Illustrationen, ISBN 978-3-658-29590-5 (Research) DOI: 10.1007/978-3-658-29591-2
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Signatur: UBN/SK 950 B644
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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 (utb ; 5251 : Informatik, Naturwissenschaften)
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Signatur: LN-U 10-19353
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Christensen, Ronald: Advanced Linear Modeling : Statistical Learning and Dependent Data / by Ronald Christensen.. - 3rd ed. 2019.. - Cham: Springer International Publishing, 2019. - 1 Online-Ressource(XXIII, 608 p. 76 illus., 6 illus. in color.), ISBN 978-3-030-29164-8 (Springer eBook Collection) (Springer Texts in Statistics) DOI: 10.1007/978-3-030-29164-8
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Louen, Chris Jan: Datenbasierte Zustandsüberwachung in Personenkraftfahrzeugen mit Anwendung an einem Drei-Wege-Katalysator / von Chris Jan Louen. - Wiesbaden: Springer Vieweg, 2016. - Online-Ressource (XIII, 122 S. 67 Abb, online resource), ISBN 978-3-658-14445-6 (SpringerLink : Bücher) DOI: 10.1007/978-3-658-14445-6
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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. - New York ; Heidelberg ; Dordrecht ; London: Springer, [2017]. - xiv, 426 Seiten : Diagramme, ISBN 978-1-4614-7137-0 (Springer texts in statistics) DOI: 10.1007/978-1-4614-7138-7
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Signatur: LN-U 8-17228::(N,8)
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Schölkopf, Bernhard: Learning with kernels : support vector machines, regularization, optimization, and beyond / Bernhard Schölkopf, Alexander J. Smola. - [Reprint.]. - Cambridge, Mass. [u.a.]: MIT Press, 2002. - XVIII, 626 Seiten : Ill., graph. Darst., ISBN 978-0-262-19475-4 (Adaptive computation and machine learning series)
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vor Ort ausleih- oder vormerkbar
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Christensen, Ronald: Advanced linear modeling : statistical learning and dependent data / Ronald Christensen. - Third edition. - Cham, Switzerland: Springer, [2019]. - xxiii, 608 Seiten : Diagramme, ISBN 978-3-030-29163-1 (Springer texts in statistics) DOI: 10.1007/978-3-030-29164-8
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Signatur: UBN/SK 840 C554(3)
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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 (Adaptive computation and machine learning series)
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Johannes, Marc: Integration of prior biological knowledge into Support Vector Machines / Marc Johannes, 2011. - Online-Ressource DOI: 10.11588/heidok.00012434
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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.. - Cambridge [u.a.]: Cambridge Univ. Press, 2006. - XIII, 189 S. : Ill., graph. Darst., ISBN 978-0-521-78019-3
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Kutsia, Merab: Learning and generalisation in neural networks with local preprocessing / Merab Kutsia, 2007. - Online-Ressource
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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 (Series in machine perception and artificial intelligence ; 68)
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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). - Cambridge [u.a.]: Cambridge University Press, 2000. - XIII, 189 S : Ill., graph. Darst, ISBN 978-0-521-78019-3
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Signatur: UBN/ST 300 C933
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Rule extraction from support vector machines / Joachim Diederich (ed.). - Berlin ; Heidelberg: Springer, 2008. - XII, 262 S. : Ill., graph. Darst., ISBN 978-3-540-75389-6 (Studies in computational intelligence ; 80)
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Signatur: LN-U 10-14670
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