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» An Algorithm for Iterative Selection of Blocks of Features
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71
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IDA
1997
Springer
15 years 6 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
150
Voted
KDD
2007
ACM
276views Data Mining» more  KDD 2007»
16 years 3 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
137
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ICIP
2008
IEEE
15 years 9 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
138
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IVC
2007
131views more  IVC 2007»
15 years 2 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 ...
145
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BMCBI
2010
154views more  BMCBI 2010»
15 years 2 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