Balancing flexibility and robustness in machine learning: semi-parametric methods and sparse linear models
作者： José Miguel Hernández-Lobato
DOI:
关键词:
摘要: Tesis doctoral inedita. Universidad Autonoma de Madrid, Escuela Politecnica Superior, noviembre 2010
unirioja.es
wordpress.com 参考文章(166)
Karsten Prause, The Generalized Hyperbolic Model: Estimation, Financial Derivatives, and Risk Measures ,(1999)
Sebastian Raible, Lévy Processes in Finance: Theory, Numerics, and Empirical Facts ,(2000)
Jean-Julien Aucouturier, Laurent Daudet, Editorial: Pattern recognition of non-speech audio Pattern Recognition Letters. ,vol. 31, pp. 1487- 1488 ,(2010) , 10.1016/J.PATREC.2010.05.003
Ludmila I. Kuncheva, A stability index for feature selection conference on artificial intelligence for applications. pp. 390- 395 ,(2007)
Yuan Ming, Hui Zou, The F ∞ -norm support vector machine Statistica Sinica. ,vol. 18, pp. 379- 398 ,(2008)
Christopher K. I. Williams, Carl Edward Rasmussen, Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) The MIT Press. ,(2005)
Robert E. McCulloch, Edward I. George, APPROACHES FOR BAYESIAN VARIABLE SELECTION Statistica Sinica. ,vol. 7, pp. 339- 373 ,(1997)
Didier Sornette, Yannick Malevergne, Extreme Financial Risks: From Dependence to Risk Management ,(2005)
Zekai Sen, Spatial Modeling Principles in Earth Sciences Springer International Publishing. ,(2016) , 10.1007/978-1-4020-9672-3
Skew-Elliptical Distributions and Their Applications : A Journey Beyond Normality Chapman and Hall/CRC. ,(2004) , 10.1201/9780203492000