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» Kolmogorov Complexity and Model Selection
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ML
2002
ACM
178views Machine Learning» more  ML 2002»
13 years 7 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
FSKD
2008
Springer
119views Fuzzy Logic» more  FSKD 2008»
13 years 8 months ago
An Entropy Decision Model for Selection of QoS-Aware Services Provisioning
Evaluating web services provisioning is able to reduce redundancy in search and also generates advantages to service providers who deliver valuable services. However, there exist ...
Ping Wang
ICPR
2004
IEEE
14 years 8 months ago
Selecting Models from Videos for Appearance-Based Face Recognition
In this paper, we propose an unsupervised approach to select representative face samples (models) from raw videos and build an appearance-based face recognition system. The approa...
Abdenour Hadid, Matti Pietikäinen
TSP
2010
13 years 2 months ago
Doubly selective channel estimation using exponential basis models and subblock tracking
Three versions of a novel adaptive channel estimation approach, exploiting the over-sampled complex exponential basis expansion model (CE-BEM), is presented fordoubly selectivechan...
Jitendra K. Tugnait, Shuangchi He, Hyosung Kim
TIP
2008
205views more  TIP 2008»
13 years 7 months ago
Image Denoising Using Derotated Complex Wavelet Coefficients
A method for removing additive Gaussian noise from digital images is described. It is based on statistical modeling of the coefficients of a redundant, oriented, complex multiscale...
Mark Miller, Nick G. Kingsbury