소장자료
LDR | 02977cam a2200000 a | ||
001 | 0100497166▲ | ||
005 | 20210205105800▲ | ||
007 | ta ▲ | ||
008 | 140910s2015 flua b 101 0 eng c▲ | ||
020 | ▼a9781482241396 (hbk.)▲ | ||
035 | ▼a(KERIS)REF000017549646▲ | ||
040 | ▼aD LC▼beng▼cDLC▼dDLC▼d221016▲ | ||
082 | 0 | 4 | ▼a511/.8▼223▲ |
090 | ▼a511.8▼bR344s▲ | ||
245 | 1 | 0 | ▼aRegularization, optimization, kernels, and support vector machines /▼cedited by, Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou.▲ |
260 | ▼aBoca Raton :▼bCRC Press/Taylor & Francis,▼c2015.▲ | ||
300 | ▼axvii, 507 p. ;▼bill. ;▼c25 cm.▲ | ||
504 | ▼aIncludes bibliographical references and index.▲ | ||
520 | ▼a"Obtaining reliable models from given data is becoming increasingly important in a wide range of different applications fields including the prediction of energy consumption, complex networks, environmental modelling, biomedicine, bioinformatics, finance, process modelling, image and signal processing, brain-computer interfaces, and others. In data-driven modelling approaches one has witnessed considerable progress in the understanding of estimating flexible nonlinear models, learning and generalization aspects, optimization methods, and structured modelling. One area of high impact both in theory and applications is kernel methods and support vector machines. Optimization problems, learning, and representations of models are key ingredients in these methods. On the other hand, considerable progress has also been made on regularization of parametric models, including methods for compressed sensing and sparsity, where convex optimization plays an important role. At the international workshop ROKS 2013 Leuven, 1 July 8-10, 2013, researchers from diverse fields were meeting on the theory and applications of regularization, optimization, kernels, and support vector machines. At this occasion the present book has been edited as a follow-up to this event, with a variety of invited contributions from presenters and scientific committee members. It is a collection of recent progress and advanced contributions on these topics, addressing methods including ..."--Provided by publisher.▲ | ||
650 | 0 | ▼aMathematical models▼vCongresses.▲ | |
650 | 0 | ▼aMathematical statistics▼vCongresses.▲ | |
650 | 0 | ▼aCompressed sensing (Telecommunication)▲ | |
700 | 1 | ▼aSuykens, Johan A. K.▲ | |
700 | 1 | ▼aSignoretto, Marco.▲ | |
700 | 1 | ▼aArgyriou, Andreas.▲ |
Regularization, optimization, kernels, and support vector machines
자료유형
국외단행본
서명/책임사항
Regularization, optimization, kernels, and support vector machines / edited by, Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou.
발행사항
Boca Raton : CRC Press/Taylor & Francis , 2015.
형태사항
xvii, 507 p. ; ill. ; 25 cm.
서지주기
Includes bibliographical references and index.
요약주기
"Obtaining reliable models from given data is becoming increasingly important in a wide range of different applications fields including the prediction of energy consumption, complex networks, environmental modelling, biomedicine, bioinformatics, finance, process modelling, image and signal processing, brain-computer interfaces, and others. In data-driven modelling approaches one has witnessed considerable progress in the understanding of estimating flexible nonlinear models, learning and generalization aspects, optimization methods, and structured modelling. One area of high impact both in theory and applications is kernel methods and support vector machines. Optimization problems, learning, and representations of models are key ingredients in these methods. On the other hand, considerable progress has also been made on regularization of parametric models, including methods for compressed sensing and sparsity, where convex optimization plays an important role. At the international workshop ROKS 2013 Leuven, 1 July 8-10, 2013, researchers from diverse fields were meeting on the theory and applications of regularization, optimization, kernels, and support vector machines. At this occasion the present book has been edited as a follow-up to this event, with a variety of invited contributions from presenters and scientific committee members. It is a collection of recent progress and advanced contributions on these topics, addressing methods including ..."--Provided by publisher.
주제
ISBN
9781482241396 (hbk.)
청구기호
511.8 R344s
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