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ICPR
2006
IEEE
16 years 12 days ago
On Kernel Selection in Relevance Vector Machines Using Stability Principle
In this paper we propose an alternative interpretation of Bayesian learning based on maximal evidence principle. We establish a notion of local evidence which can be viewed as a c...
Dmitry Kropotov, Nikita Ptashko, Oleg Vasiliev, Dm...
ICML
2008
IEEE
16 years 3 days ago
Sparse multiscale gaussian process regression
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of th...
Bernhard Schölkopf, Christian Walder, Kwang I...
BMCBI
2007
139views more  BMCBI 2007»
14 years 11 months ago
Improving model predictions for RNA interference activities that use support vector machine regression by combining and filterin
Background: RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathwa...
Andrew S. Peek
TNN
2010
176views Management» more  TNN 2010»
14 years 6 months ago
Sparse approximation through boosting for learning large scale kernel machines
Abstract--Recently, sparse approximation has become a preferred method for learning large scale kernel machines. This technique attempts to represent the solution with only a subse...
Ping Sun, Xin Yao
IWANN
2005
Springer
15 years 4 months ago
Load Forecasting Using Fixed-Size Least Squares Support Vector Machines
Based on the Nystr¨om approximation and the primal-dual formulation of Least Squares Support Vector Machines (LS-SVM), it becomes possible to apply a nonlinear model to a large sc...
Marcelo Espinoza, Johan A. K. Suykens, Bart De Moo...