TNN
2010
12 years 11 months ago
2010
Abstract--A crucial issue in designing learning machines is to select the correct model parameters. When the number of available samples is small, theoretical sample-based generali...
JMLR
2010
12 years 11 months ago
2010
Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model selection criterion, often based on an estimator of...
ICIP
2010
IEEE
13 years 2 months ago
2010
IEEE
An image representation framework based on structured sparse model selection is introduced in this work. The corresponding modeling dictionary is comprised of a family of learned ...
CVPR
2010
IEEE
13 years 2 months ago
2010
IEEE
Our goal is to fit the multiple instances (or structures) of a generic model existing in data. Here we propose a novel model selection scheme to estimate the number of genuine str...
COLT
2010
Springer
13 years 2 months ago
2010
Springer
We analyze the regret, measured in terms of log loss, of the maximum likelihood (ML) sequential prediction strategy. This "follow the leader" strategy also defines one o...
BMVC
2010
13 years 2 months ago
2010
Man-made environments are abundant with planar surfaces which have attractive properties and are a prerequisite for a variety of vision tasks. This paper presents an incremental m...
PAMI
2010
13 years 3 months ago
2010
—Adapting the hyperparameters of support vector machines (SVMs) is a challenging model selection problem, especially when flexible kernels are to be adapted and data are scarce....
ML
2010
ACM
13 years 3 months ago
2010
ACM
Abstract Clustering Stability methods are a family of widely used model selection techniques for data clustering. Their unifying theme is that an appropriate model should result in...
MA
2010
Springer
13 years 3 months ago
2010
Springer
Model selection by the predictive least squares (PLS) principle has been thoroughly studied in the context of regression model selection and autoregressive (AR) model order estima...
IJAR
2010
13 years 3 months ago
2010
We consider the problem of learning Bayesian network models in a non-informative setting, where the only available information is a set of observational data, and no background kn...