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» Learning Approximate Consistencies
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BC
1998
94views more  BC 1998»
15 years 6 months ago
Quantization of human motions and learning of accurate movements
This paper presents a mathematical model for the learning of accurate human arm movements. Its main features are that the movement is the superposition of smooth submovements, the ...
Etienne Burdet, Theodore E. Milner
MICCAI
2008
Springer
16 years 8 months ago
Streamline Flows for White Matter Fibre Pathway Segmentation in Diffusion MRI
We introduce a fibre tract segmentation algorithm based on the geometric coherence of fibre orientations as indicated by a streamline flow model. The inference of local flow approx...
Peter Savadjiev, Jennifer S. W. Campbell, G. Bru...
175
Voted
DEXAW
2005
IEEE
156views Database» more  DEXAW 2005»
16 years 20 days ago
Fixing Inconsistent Databases by Updating Numerical Attributes
For several reasons a database may not satisfy certain integrity constraints (ICs). However, most likely most of the information in it is still consistent with the ICs; and could ...
Leopoldo E. Bertossi, Loreto Bravo, Enrico Francon...
205
Voted
COCOON
2006
Springer
15 years 10 months ago
Finding Small OBDDs for Incompletely Specified Truth Tables Is Hard
We present an efficient reduction mapping undirected graphs G with n = 2k vertices for integers k to tables of partially specified Boolean functions g : {0, 1}4k+1 {0, 1, } so tha...
Jesper Torp Kristensen, Peter Bro Miltersen
KDD
2008
ACM
137views Data Mining» more  KDD 2008»
16 years 7 months ago
Learning classifiers from only positive and unlabeled data
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Charles Elkan, Keith Noto