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» An Algorithm for Iterative Selection of Blocks of Features
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IDA
1997
Springer
13 years 10 months ago
How to Find Big-Oh in Your Data Set (and How Not to)
The empirical curve bounding problem is de ned as follows. Suppose data vectors X Y are presented such that E(Y i]) = f(X i]) where f(x) is an unknown function. The problem is to a...
Catherine C. McGeoch, Doina Precup, Paul R. Cohen
KDD
2007
ACM
276views Data Mining» more  KDD 2007»
14 years 7 months ago
Nonlinear adaptive distance metric learning for clustering
A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a lowdimensio...
Jianhui Chen, Zheng Zhao, Jieping Ye, Huan Liu
ICIP
2008
IEEE
14 years 1 months ago
Hardware-friendly descreening
Conventional electrophotographic printers tend to produce Moir´e artifacts when used for printing images scanned from printed material such as books and magazines. We propose a n...
Hasib Siddiqui, Mireille Boutin, Charles A. Bouman
IVC
2007
131views more  IVC 2007»
13 years 6 months ago
Multi-view correspondence by enforcement of rigidity constraints
Establishing the correct correspondence between features in an image set remains a challenging problem amongst computer vision researchers. In fact, the combinatorial nature of fe...
Ricardo Oliveira, João Xavier, João ...
BMCBI
2010
154views more  BMCBI 2010»
13 years 6 months ago
Binary image representation of a ligand binding site: its application to efficient sampling of a conformational ensemble
Background: Modelling the ligand binding site of a protein is an important component of understanding proteinligand interactions and is being actively studied. Even if the side ch...
Edon Sung, Sangsoo Kim, Whanchul Shin