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» Approximation Methods for Supervised Learning
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NIPS
2007
13 years 11 months ago
Random Features for Large-Scale Kernel Machines
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The feat...
Ali Rahimi, Benjamin Recht
ICIP
2010
IEEE
13 years 8 months ago
Image analysis with regularized Laplacian eigenmaps
Many classes of image data span a low dimensional nonlinear space embedded in the natural high dimensional image space. We adopt and generalize a recently proposed dimensionality ...
Frank Tompkins, Patrick J. Wolfe
LWA
2007
13 years 11 months ago
Parameter Learning for a Readability Checking Tool
This paper describes the application of machine learning methods to determine parameters for DeLite, a readability checking tool. DeLite pinpoints text segments that are difficul...
Tim vor der Brück, Johannes Leveling
ICASSP
2011
IEEE
13 years 1 months ago
A method to infer emotions from facial Action Units
We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and ...
Sudha Velusamy, Hariprasad Kannan, Balasubramanian...
STOC
2006
ACM
122views Algorithms» more  STOC 2006»
14 years 10 months ago
Fast convergence to Wardrop equilibria by adaptive sampling methods
We study rerouting policies in a dynamic round-based variant of a well known game theoretic traffic model due to Wardrop. Previous analyses (mostly in the context of selfish routi...
Simon Fischer, Harald Räcke, Berthold Vö...