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TNN
2010
143views Management» more  TNN 2010»
8 years 4 months ago
Using unsupervised analysis to constrain generalization bounds for support vector classifiers
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...
Sergio Decherchi, Sandro Ridella, Rodolfo Zunino, ...
JMLR
2010
118views more  JMLR 2010»
8 years 4 months ago
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model selection criterion, often based on an estimator of...
Gavin C. Cawley, Nicola L. C. Talbot
ICIP
2010
IEEE
8 years 7 months ago
Image modeling and enhancement via structured sparse model selection
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 ...
Guoshen Yu, Guillermo Sapiro, Stéphane Mall...
CVPR
2010
IEEE
8 years 7 months ago
Multi-structure model selection via kernel optimisation
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...
Tat-Jun Chin, David Suter, Hanzi Wang
COLT
2010
Springer
8 years 7 months ago
Following the Flattened Leader
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...
Wojciech Kotlowski, Peter Grünwald, Steven de...
BMVC
2010
8 years 7 months ago
Incremental Model Selection for Detection and Tracking of Planar Surfaces
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...
Johann Prankl, Michael Zillich, Bastian Leibe, Mar...
PAMI
2010
132views more  PAMI 2010»
8 years 7 months ago
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
—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....
Tobias Glasmachers, Christian Igel
ML
2010
ACM
127views Machine Learning» more  ML 2010»
8 years 7 months ago
Stability and model selection in k-means clustering
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...
Ohad Shamir, Naftali Tishby
MA
2010
Springer
132views Communications» more  MA 2010»
8 years 7 months ago
Model selection by sequentially normalized least squares
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...
Jorma Rissanen, Teemu Roos, Petri Myllymäki
IJAR
2010
130views more  IJAR 2010»
8 years 7 months ago
Learning locally minimax optimal Bayesian networks
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...
Tomi Silander, Teemu Roos, Petri Myllymäki
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