Finite-Dimensional Projection for Classification and Statistical Learning

By Gilles Blanchard, Laurent Zwald. IEEE Transactions on Information Theory, 2008

595 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

EAACK—A Secure Intrusion-Detection System for MANETs (2013, methodology): “TWOACK proposed by Liu et al. [16] is one of the most important approaches among them.”
A regularized kernel-based approach to unsupervised audio segmentation (2009, unknown): “We propose to use instead a spectral truncation type of regularization: I( ˆ ΣW ; 1/d) = d X p=1 λ −1 p (ep ⊗ ep) , (4) which corresponds to the inverse of a finite-dimensional approximation on Span{e1, . . . , e d } ofˆΣW (see also [13]).”
A Survey of Modern Questions and Challenges in Feature Extraction (2015, unknown): “Moreover, supervised learning of projections in a fixed RKHS have proven to be effective for nonlinear feature extraction (Blanchard and Zwald, 2008; Fukumizu et al., 2004) as well as for multiple kernel learning (Gönen and Alpaydın, 2011; Gönen, 2014).”