Robust <mml:math altimg="si3.gif" overflow="scroll" xmlns:xocs="http://www.elsevier.com/xml/xocs/dtd" xmlns:xs="http://www.w3.org/2001/XMLSchema" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.elsevier.com/xml/ja/dtd" xmlns:ja="http://www.elsevier.com/xml/ja/dtd" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:tb="http://www.elsevier.com/xml/common/table/dtd" xmlns:sb="http://www.elsevier.com/xml/common/struct-bib/dtd" xmlns:ce="http://www.elsevier.com/xml/common/dtd" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:cals="http://www.elsevier.com/xml/common/cals/dtd"><mml:mi>γ</mml:mi></mml:math>-filter using support vector machines-Reference-Cited by-同舟云学术

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Robust γ-filter using support vector machines

Published:2004-12 Issue: Volume:62 Page:493-499
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Camps-Valls G.,Martínez-Ramón M.,Rojo-Álvarez J.L.,Soria-Olivas E.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference12 articles.

1. Nonlinear modeling and forecasting;Casdagli,1992

2. T.W. Chiang, The analytical solution of gamma filter model, in: International Conference on Neural Networks, vol. 2, Houston, TX, USA, June 1997, pp. 685–689.

3. Methods of Mathematical Physics;Courant,1953

4. Simulation of Communication Systems;Jeruchim,1992

5. Nonlinear Time Series Analysis;Kantz,1997

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3. Discovery of motifs to forecast outlier occurrence in time series;Pattern Recognition Letters;2011-09

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5. Learning non-linear time-scales with kernel -filters;Neurocomputing;2009-01

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