P. L. Lai and C. Fyfe. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 10(5):365--377, 2000. Home/Search Document Not in Database Summary Related Articles Check |
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Efficient Leave-One-Out Cross-Validation of Kernel Fisher.. - Cawley, Talbot (2003) (Correct)
.... to Elsevier Science 24 March 2003 1 Introduction The now familiar kernel trick has been used to derive non linear variants of many linear methods borrowed from classical statistics (e.g. 2, 3] including ridge regression [4] principal component analysis [5] and canonical correlation analysis [6] as well as more recent developments such as the maximal margin classifier [7] giving rise to the support vector machine [8] These methods have come to be known collectively as kernel machines and have attracted considerable interest in the machine learning research community due to a ....
P. L. Lai and C. Fyfe. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 10(5):365--377, 2000. 17
Efficient Leave-One-Out Cross-Validation of Kernel Fisher.. - Cawley, Talbot (2003) (Correct)
.... to Elsevier Science 24 March 2003 1 Introduction The now familiar kernel trick has been used to derive non linear variants of many linear methods borrowed from classical statistics (e.g. 2, 3] including ridge regression [4] principal component analysis [5] and canonical correlation analysis [6] as well as more recent developments such as the maximal margin classifier [7] giving rise to the support vector machine [8] These methods have come to be known collectively as kernel machines and have attracted considerable interest in the machine learning research community due to a ....
P. L. Lai and C. Fyfe. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 10(5):365--377, 2000. 17
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....the variables onto these basis vectors are mutually maximised. In an attempt to increase the flexibility of the feature selection, kernelisation of CCA (KCCA) has been applied to map the hypotheses to a higher dimensional feature space. KCCA has been applied in some preliminary work by Fyfe Lai [8], Akaho [1] and the recently Vinokourov et al. 19] with improved results. During recent years there has been a vast increase in the amount of multimedia content available both o# line and online, though we are unable to access or make use of this data unless it is organised in such a way as to ....
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Pei Ling Lai & Colin Fyfe. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, vol. 10, no. 5, pages 365--377, 2000.
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P. L. Lai and C. Fyfe. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 10(5):365--377, 2000.
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P. L. Lai and C. Fyfe. Kernel and nonlinear canonical correlation analysis. Int. J. Neural Sys., 10(5):365--377, 2000.
Learning Shared Latent Structure for Image - Synthesis And Robotic (Correct)
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P. L. Lai and C. Fyfe. Kernel and nonlinear canonical correlation analysis. Int. J. Neural Sys., 10(5):365--377, 2000.
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Pei Ling Lai and Colin Fyfe. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 10(5):365--377, 2000.
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P. Lai and C. Fyfe. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 10(5):365377, 2000.
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P.L. Lai and C. Fyfe. Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, submitted for publication.
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P. L. Lai and C. Fyfe, "Kernel and nonlinear canonical correlation analysis," International Journal of Neural Systems, vol. 10, no. 5, pp. 365--377, 2000.
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P. L. Lai, C. Fyfe, Kernel and nonlinear canonical correlation analysis, International Journal of Neural Systems 10 (5) (2000) 365--377.
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