Sciweavers

Free Online Productivity Tools i2Speak i2Symbol i2OCR iTex2Img iWeb2Print iWeb2Shot i2Type iPdf2Split iPdf2Merge i2Bopomofo i2Arabic i2Style i2Image i2PDF iLatex2Rtf Sci2ools

9

UAI
2004

favoriteEmaildiscussreport

114views Artificial Intelligence» more UAI 2004»

On-line Prediction with Kernels and the Complexity Approximation Principle

13 years 6 months ago

On-line Prediction with Kernels and the Complexity Approximation Principle

Download uai.sis.pitt.edu

The paper describes an application of Aggregating Algorithm to the problem of regression. It generalizes earlier results concerned with plain linear regression to kernel techniques and presents an on-line algorithm which performs nearly as well as any oblivious kernel predictor. The paper contains the derivation of an estimate on the performance of this algorithm. The estimate is then used to derive an application of the Complexity Approximation Principle to kernel methods.

Alexander Gammerman, Yuri Kalnishkan, Vladimir Vov

Real-time Traffic

Algorithm | Oblivious Kernel Predictor | Plain Linear Regression | UAI 2004 | UAI 2008 |

claim paper

Related Content

» ComplexityRegularized Denoising of PoissonCorrupted Data

» Prediction of Protein Secondary Structures with a Novel Kernel Density Estimator

» Bayesian binning beats approximate alternatives estimating peristimulus time histograms

» Gradient boosting for kernelized output spaces

» Healing the relevance vector machine through augmentation

» Gaussian Processes Multiple Instance Learning

» Optimal delayed decoding of predictively encoded sources

» Model selection by sequentially normalized least squares

» Structured OutputAssociative Regression

Post Info
More Details (n/a)

Added	31 Oct 2010
Updated	31 Oct 2010
Type	Conference
Year	2004
Where	UAI
Authors	Alexander Gammerman, Yuri Kalnishkan, Vladimir Vovk

Comments (0)