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An Introduction to Kernel Methods
2009Machine learning has experienced a great advance in the eighties and nineties due to the active research in artificial neural networks and adaptive systems. These tools have demonstrated good results in many real applications, since neither a priori knowledge about the distribution of the available data nor the relationships among the independent ...
Gustavo Camps-Valls +2 more
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Advances in kernel methods: support vector learning
, 1999Introduction to support vector learning roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and ...
B. Scholkopf, C. Burges, Alex Smola
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Applications of Kernel Methods
2009In this chapter, we give a survey of applications of the kernel methods introduced in the previous chapter. We focus on different application domains that are particularly active in both direct application of well-known kernel methods, and in new algorithmic developments suited to a particular problem.
Gustavo Camps-Valls +2 more
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Multivariate binary discrimination by the kernel method
, 1976SUMMARY An extension of the kernel method of density estimation from continuous to multivariate binary spaces is described. Its simple nonparametric nature together with its consistency properties make it an attractive tool in discrimination problems ...
J. Aitchison, C. Aitken
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Kernel Methods in Chemoinformatics
2005Millions of people are suffering from fatal diseases such as cancer, AIDS, and many other bacterial and viral illnesses. The key issue is now how to design lifesaving and cost-effective drugs so that the diseases can be cured and prevented. It would also enable the provision of medicines in developing countries, where approximately 80% of the world ...
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2012
Spline models are based on piecewise polynomial fitting, while kernel regression models are based on local polynomial fitting. These two approaches to modeling are extremely popular, and so we dedicate a whole chapter to their description.
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Spline models are based on piecewise polynomial fitting, while kernel regression models are based on local polynomial fitting. These two approaches to modeling are extremely popular, and so we dedicate a whole chapter to their description.
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IFAC Proceedings Volumes, 2003
Abstract A disadvantage of many statistical modelling techniques is that the resulting model is extremely difficult to interpret. A number of new concepts and algorithms have been introduced by researchers to address this problem. They focus primarily on determining which inputs arc relevant in predicting the output. This work describes a transparent,
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Abstract A disadvantage of many statistical modelling techniques is that the resulting model is extremely difficult to interpret. A number of new concepts and algorithms have been introduced by researchers to address this problem. They focus primarily on determining which inputs arc relevant in predicting the output. This work describes a transparent,
openaire +2 more sources
Reproducing Kernel Method for Fractional Derivative with Non-local and Non-singular Kernel
Studies in Systems, Decision and Control, 2019Ali Akgül
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An iterative approximation for time-fractional Cahn–Allen equation with reproducing kernel method
Computational and Applied Mathematics, 2018M. G. Sakar, Onur Saldır, F. Erdoğan
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IEEE transactions on neural systems and rehabilitation engineering, 2017
Xing Chen, Yan Zeng, Yuehong Yin
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Xing Chen, Yan Zeng, Yuehong Yin
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