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126
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PROMISE
2010
14 years 10 months ago
On the value of learning from defect dense components for software defect prediction
BACKGROUND: Defect predictors learned from static code measures can isolate code modules with a higher than usual probability of defects. AIMS: To improve those learners by focusi...
Hongyu Zhang, Adam Nelson, Tim Menzies
126
Voted
ISMB
1993
15 years 4 months ago
Transmembrane Segment Prediction from Protein Sequence Data
Weconsider tile automatedidentification of transmembrane domains in membrane protein sequences. 324 proteins (containing 1585 segrrmnts) werc examined, representing every protein ...
Sholom M. Weiss, Dawn M. Cohen, Nitin Indurkhya
134
Voted
RAS
2008
123views more  RAS 2008»
15 years 3 months ago
Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping
This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates th...
Martin Persson, Tom Duckett, Achim J. Lilienthal
KDD
2010
ACM
245views Data Mining» more  KDD 2010»
15 years 5 months ago
Learning incoherent sparse and low-rank patterns from multiple tasks
We consider the problem of learning incoherent sparse and lowrank patterns from multiple tasks. Our approach is based on a linear multi-task learning formulation, in which the spa...
Jianhui Chen, Ji Liu, Jieping Ye
136
Voted
ICML
2009
IEEE
15 years 10 months ago
Learning linear dynamical systems without sequence information
Virtually all methods of learning dynamic systems from data start from the same basic assumption: that the learning algorithm will be provided with a sequence, or trajectory, of d...
Tzu-Kuo Huang, Jeff Schneider