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001441550 001__ 1441550
001441550 003__ OCoLC
001441550 005__ 20230309004745.0
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001441550 019__ $$a1290814767$$a1290839347$$a1294359764
001441550 020__ $$a9783030821715$$q(electronic bk.)
001441550 020__ $$a3030821714$$q(electronic bk.)
001441550 020__ $$z9783030821708$$q(print)
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001441550 0247_ $$a10.1007/978-3-030-82171-5$$2doi
001441550 035__ $$aSP(OCoLC)1290893472
001441550 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dOCLCO$$dDCT$$dOCLCF$$dOCLCO$$dN$T$$dUKAHL$$dOCLCQ
001441550 049__ $$aISEA
001441550 050_4 $$aQA278.2
001441550 08204 $$a519.5/36$$223
001441550 1001_ $$aOwhadi, Houman.$$eauthor.$$1https://orcid.org/0000-0002-5677-1600
001441550 24510 $$aKernel mode decomposition and the programming of kernels /$$cHouman Owhadi, Clint Scovel, Gene Ryan Yoo.
001441550 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001441550 300__ $$a1 online resource (x, 118 pages) :$$billustrations (some color).
001441550 336__ $$atext$$btxt$$2rdacontent
001441550 337__ $$acomputer$$bc$$2rdamedia
001441550 338__ $$aonline resource$$bcr$$2rdacarrier
001441550 347__ $$atext file$$bPDF$$2rda
001441550 4901_ $$aSurveys and tutorials in the applied mathematical sciences,$$x2199-4773 ;$$vvolume 8
001441550 504__ $$aIncludes bibliographical references and index.
001441550 5050_ $$aIntroduction -- Review -- The mode decomposition problem -- Kernel mode decomposition networks (KMDNets) -- Additional programming modules and squeezing -- Non-trigonometric waveform and iterated KMD -- Unknown base waveforms -- Crossing frequencies, vanishing modes, and noise -- Appendix.
001441550 506__ $$aAccess limited to authorized users.
001441550 520__ $$aThis monograph demonstrates a new approach to the classical mode decomposition problem through nonlinear regression models, which achieve near-machine precision in the recovery of the modes. The presentation includes a review of generalized additive models, additive kernels/Gaussian processes, generalized Tikhonov regularization, empirical mode decomposition, and Synchrosqueezing, which are all related to and generalizable under the proposed framework. Although kernel methods have strong theoretical foundations, they require the prior selection of a good kernel. While the usual approach to this kernel selection problem is hyperparameter tuning, the objective of this monograph is to present an alternative (programming) approach to the kernel selection problem while using mode decomposition as a prototypical pattern recognition problem. In this approach, kernels are programmed for the task at hand through the programming of interpretable regression networks in the context of additive Gaussian processes. It is suitable for engineers, computer scientists, mathematicians, and students in these fields working on kernel methods, pattern recognition, and mode decomposition problems.
001441550 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed January 5, 2022).
001441550 650_0 $$aRegression analysis.
001441550 650_0 $$aKernel functions.
001441550 650_0 $$aDecomposition (Mathematics)
001441550 650_6 $$aAnalyse de régression.
001441550 650_6 $$aNoyaux (Mathématiques)
001441550 650_6 $$aDécomposition (Mathématiques)
001441550 655_0 $$aElectronic books.
001441550 7001_ $$aScovel, Clint,$$d1955-$$eauthor.
001441550 7001_ $$aYoo, Gene Ryan,$$eauthor.$$0(orcid)0000-0002-5319-5599$$1https://orcid.org/0000-0002-5319-5599
001441550 77608 $$iPrint version:$$z3030821706$$z9783030821708$$w(OCoLC)1257890477
001441550 830_0 $$aSurveys and tutorials in the applied mathematical sciences ;$$v8.$$x2199-4773
001441550 852__ $$bebk
001441550 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-82171-5$$zOnline Access$$91397441.1
001441550 909CO $$ooai:library.usi.edu:1441550$$pGLOBAL_SET
001441550 980__ $$aBIB
001441550 980__ $$aEBOOK
001441550 982__ $$aEbook
001441550 983__ $$aOnline
001441550 994__ $$a92$$bISE