185 search results - page 7 / 37 » An empirical evaluation of supervised learning in high dimen... |
CVPR
2007
IEEE
14 years 9 months ago
2007
IEEE
The success of tensor-based subspace learning depends heavily on reducing correlations along the column vectors of the mode-k flattened matrix. In this work, we study the problem ...
JMLR
2007
13 years 7 months ago
2007
We show that, given data from a mixture of k well-separated spherical Gaussians in Rd, a simple two-round variant of EM will, with high probability, learn the parameters of the Ga...
PSIVT
2009
Springer
14 years 2 months ago
2009
Springer
PCA-SIFT is an extension to SIFT which aims to reduce SIFT’s high dimensionality (128 dimensions) by applying PCA to the gradient image patches. However PCA is not a discriminati...
MLDM
2005
Springer
14 years 1 months ago
2005
Springer
Abstract. In supervised learning, discretization of the continuous explanatory attributes enhances the accuracy of decision tree induction algorithms and naive Bayes classifier. M...
GECCO
2006
Springer
13 years 11 months ago
2006
Springer
Genetic Programming offers freedom in the definition of the cost function that is unparalleled among supervised learning algorithms. However, this freedom goes largely unexploited...