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» Evaluating algorithms that learn from data streams
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CPAIOR
2009
Springer
16 years 1 months ago
Learning How to Propagate Using Random Probing
Abstract. In constraint programming there are often many choices regarding the propagation method to be used on the constraints of a problem. However, simple constraint solvers usu...
Efstathios Stamatatos, Kostas Stergiou
PAMI
2008
196views more  PAMI 2008»
15 years 6 months ago
Distance Learning for Similarity Estimation
In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance function...
Jie Yu, Jaume Amores, Nicu Sebe, Petia Radeva, Qi ...
ACL
2009
15 years 4 months ago
Concise Integer Linear Programming Formulations for Dependency Parsing
We formulate the problem of nonprojective dependency parsing as a polynomial-sized integer linear program. Our formulation is able to handle non-local output features in an effici...
André L. Martins, Noah A. Smith, Eric P. Xi...
ICIP
2001
IEEE
16 years 8 months ago
An analysis of subjective quality in low bit rate video
A subjective quality evaluation was performed to quantify viewer responses to various visual defects that appear in low bit rate video, both at full and reduced frame rates. The s...
Mark Masry, Sheila S. Hemami
ICDM
2006
IEEE
84views Data Mining» more  ICDM 2006»
16 years 27 days ago
Exploratory Under-Sampling for Class-Imbalance Learning
Under-sampling is a class-imbalance learning method which uses only a subset of major class examples and thus is very efficient. The main deficiency is that many major class exa...
Xu-Ying Liu, Jianxin Wu, Zhi-Hua Zhou