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» Active Learning with Model Selection in Linear Regression
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AI
2009
Springer
14 years 3 months ago
Grid-Enabled Adaptive Metamodeling and Active Learning for Computer Based Design
Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alte...
Dirk Gorissen
BMCBI
2007
215views more  BMCBI 2007»
13 years 8 months ago
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregres
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
Rainer Opgen-Rhein, Korbinian Strimmer
ICPR
2010
IEEE
13 years 6 months ago
Regression-Based Multi-view Facial Expression Recognition
We present a regression-based scheme for multi-view facial expression recognition based on 2-D geometric features. We address the problem by mapping facial points (e.g. mouth corn...
Ognjen Rudovic, Ioannis Patras, Maja Pantic
AI
2004
Springer
13 years 8 months ago
A selective sampling approach to active feature selection
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and imp...
Huan Liu, Hiroshi Motoda, Lei Yu
JMLR
2002
106views more  JMLR 2002»
13 years 8 months ago
Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels
We present some greedy learning algorithms for building sparse nonlinear regression and classification models from observational data using Mercer kernels. Our objective is to dev...
Prasanth B. Nair, Arindam Choudhury 0002, Andy J. ...