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IWANN
2001
Springer
13 years 11 months ago
A Penalization Criterion Based on Noise Behaviour for Model Selection
Complexity-penalization strategies are one way to decide on the most appropriate network size in order to address the trade-off between overfitted and underfitted models. In this p...
Joaquín Pizarro Junquera, Pedro Galindo Ria...
JMLR
2011
192views more  JMLR 2011»
13 years 2 months ago
Minimum Description Length Penalization for Group and Multi-Task Sparse Learning
We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an...
Paramveer S. Dhillon, Dean P. Foster, Lyle H. Unga...
TSP
2011
170views more  TSP 2011»
13 years 1 months ago
Model Selection for Sinusoids in Noise: Statistical Analysis and a New Penalty Term
—Detection of the number of sinusoids embedded in noise is a fundamental problem in statistical signal processing. Most parametric methods minimize the sum of a data fit (likeli...
Boaz Nadler, Leonid Kontorovich
BMCBI
2010
178views more  BMCBI 2010»
13 years 7 months ago
Selecting high-dimensional mixed graphical models using minimal AIC or BIC forests
Background: Chow and Liu showed that the maximum likelihood tree for multivariate discrete distributions may be found using a maximum weight spanning tree algorithm, for example K...
David Edwards, Gabriel C. G. de Abreu, Rodrigo Lab...
JCP
2008
190views more  JCP 2008»
13 years 7 months ago
Real-time System Identification of Unmanned Aerial Vehicles: A Multi-Network Approach
In this paper, real-time system identification of an unmanned aerial vehicle (UAV) based on multiple neural networks is presented. The UAV is a multi-input multi-output (MIMO) nonl...
Vishwas R. Puttige, Sreenatha G. Anavatti