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UAI
2004
13 years 6 months ago
On-line Prediction with Kernels and the Complexity Approximation Principle
The paper describes an application of Aggregating Algorithm to the problem of regression. It generalizes earlier results concerned with plain linear regression to kernel technique...
Alexander Gammerman, Yuri Kalnishkan, Vladimir Vov...
ICIP
2000
IEEE
14 years 6 months ago
Complexity-Regularized Denoising of Poisson-Corrupted Data
In this paper, we apply the complexity?regularization principle to Poisson imaging. We formulate a natural distortion measure in image space, and present a connection between comp...
Juan Liu, Pierre Moulin
MLMTA
2007
13 years 6 months ago
Prediction of Protein Secondary Structures with a Novel Kernel Density Estimator
- Though prediction of protein secondary structures has been an active research issue in bioinformatics for quite a few years and many approaches have been proposed, a new challeng...
Yen-Jen Oyang, Darby Tien-Hao Chang, Yu-Yen Ou, Ha...
NIPS
2007
13 years 6 months ago
Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms
The peristimulus time histogram (PSTH) and its more continuous cousin, the spike density function (SDF) are staples in the analytic toolkit of neurophysiologists. The former is us...
Dominik Endres, Mike W. Oram, Johannes E. Schindel...
ICML
2007
IEEE
14 years 5 months ago
Gradient boosting for kernelized output spaces
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...