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» Kolmogorov Complexity and Model Selection
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TIT
1998
123views more  TIT 1998»
13 years 9 months ago
The Minimum Description Length Principle in Coding and Modeling
—We review the principles of Minimum Description Length and Stochastic Complexity as used in data compression and statistical modeling. Stochastic complexity is formulated as the...
Andrew R. Barron, Jorma Rissanen, Bin Yu
ICIP
2003
IEEE
14 years 11 months ago
Feature selection for unsupervised discovery of statistical temporal structures in video
We present algorithms for automatic feature selection for unsupervised structure discovery from video sequences. Feature selection in this scenario is hard because of the absence ...
Lexing Xie, Shih-Fu Chang, Ajay Divakaran, Huifang...
CSDA
2007
152views more  CSDA 2007»
13 years 9 months ago
Robust variable selection using least angle regression and elemental set sampling
In this paper we address the problem of selecting variables or features in a regression model in the presence of both additive (vertical) and leverage outliers. Since variable sel...
Lauren McCann, Roy E. Welsch
ICMCS
2006
IEEE
97views Multimedia» more  ICMCS 2006»
14 years 4 months ago
Fast Progressive Model Refinement Global Motion Estimation Algorithm with Prediction
Global Motion Estimation (GME) is an important part in the object-based applications. In this paper, a fast progressive model refinement (FPMR) GME algorithm is proposed. It can s...
Haifeng Wang, Jia Wang, Qingshan Liu, Hanqing Lu
CGI
1998
IEEE
14 years 2 months ago
Hierarchical Visibility Culling with Occlusion Trees
In the scope of rendering complex models with high depth complexity, it is of great importance to design outputsensitive algorithms, i.e., algorithms with the time complexity prop...
Jirí Bittner, Vlastimil Havran, Pavel Slav&...