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VAMOS
2010
Springer
15 years 4 months ago
The Variability Model of The Linux Kernel
Lack of realistic benchmarks hinders efficient design and evaluation of analysis techniques for feature models. We extract a variability model from the code base of the Linux kerne...
Steven She, Rafael Lotufo, Thorsten Berger, Andrze...
DCC
2006
IEEE
16 years 5 months ago
Compression and Machine Learning: A New Perspective on Feature Space Vectors
The use of compression algorithms in machine learning tasks such as clustering and classification has appeared in a variety of fields, sometimes with the promise of reducing probl...
D. Sculley, Carla E. Brodley
ICCV
2009
IEEE
16 years 11 months ago
Heterogeneous Feature Machines for Visual Recognition
With the recent efforts made by computer vision researchers, more and more types of features have been designed to describe various aspects of visual characteristics. Modeling s...
Liangliang Cao, Jiebo Luo, Feng Liang, Thomas S. H...
SDM
2010
SIAM
218views Data Mining» more  SDM 2010»
15 years 7 months ago
Confidence-Based Feature Acquisition to Minimize Training and Test Costs
We present Confidence-based Feature Acquisition (CFA), a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test...
Marie desJardins, James MacGlashan, Kiri L. Wagsta...
CVPR
2011
IEEE
15 years 2 months ago
Sharing Features Between Objects and Their Attributes
Visual attributes expose human-defined semantics to object recognition models, but existing work largely restricts their influence to mid-level cues during classifier training....
Sung Ju Hwang, Fei Sha, Kristen Grauman