Adaptive learning methods for nonlinear system modeling / edited by Danilo Comminiello, José C. Príncipe.

Other author Comminiello, Danilo.
Other author Príncipe, J. C. (José C.)
Format Electronic
Publication InfoKidlington, Oxford, United Kingdom ; Cambridge, MA, United States : Butterworth-Heinemann, an imprint of Elsevier, [2018]
Descriptionxix, 367 pages : illustrations ; 24 cm
Supplemental ContentFull text available from eBooks on EBSCOhost
Supplemental ContentFull text available from eBook - Engineering 2018
Subjects

Contents Note continued: 8.2.4.Semiparametric Reconstruction -- 8.2.5.Numerical Tests -- 8.3.Inference of Dynamic Functions Over Dynamic Graphs -- 8.3.1.Kernels on Extended Graphs -- 8.3.2.Multikernel Kriged Kalman Filters -- 8.3.3.Numerical Tests -- 8.3.4.Summary -- Acknowledgments -- References -- pt. 3 NONLINEAR MODELING WITH MULTIPLE LEARNING MACHINES -- ch. 9 Online Nonlinear Modeling via Self-Organizing Trees -- 9.1.Introduction -- 9.2.Self-Organizing Trees for Regression Problems -- 9.2.1.Notation -- 9.2.2.Construction of the Algorithm -- 9.2.3.Convergence of the Algorithm -- 9.3.Self-Organizing Trees for Binary Classification Problems -- 9.3.1.Construction of the Algorithm -- 9.3.2.Convergence of the Algorithm -- 9.4.Numerical Results -- 9.4.1.Numerical Results for Regression Problems -- 9.4.2.Numerical Results for Classification Problems -- Appendix 9.A -- 9.A.1.Proof of Theorem 1 -- 9.A.2.Proof of Theorem 2 -- Acknowledgments -- References
Bibliography noteIncludes bibliographical references and index.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Issued in other formebook version : 9780128129777
Genre/formElectronic books.
LCCN 2018947070
ISBN012812976X paperback
ISBN9780128129760 paperback

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