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» Iterative RELIEF for feature weighting
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ML
2006
ACM
131views Machine Learning» more  ML 2006»
13 years 7 months ago
Markov logic networks
We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge b...
Matthew Richardson, Pedro Domingos
KDD
2008
ACM
192views Data Mining» more  KDD 2008»
14 years 8 months ago
Partial least squares regression for graph mining
Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect inform...
Hiroto Saigo, Koji Tsuda, Nicole Krämer
SEMWEB
2010
Springer
13 years 5 months ago
One Size Does Not Fit All: Customizing Ontology Alignment Using User Feedback
Abstract. A key problem in ontology alignment is that different ontological features (e.g., lexical, structural or semantic) vary widely in their importance for different ontology ...
Songyun Duan, Achille Fokoue, Kavitha Srinivas
ICN
2005
Springer
14 years 1 months ago
Scheduling Algorithms for Input Queued Switches Using Local Search Technique
Input Queued switches have been very well studied in the recent past. The Maximum Weight Matching (MWM) algorithm is known to deliver 100% throughput under any admissible traffic. ...
Yanfeng Zheng, Simin He, Shutao Sun, Wen Gao
ICCV
2007
IEEE
14 years 9 months ago
Gradient Feature Selection for Online Boosting
Boosting has been widely applied in computer vision, especially after Viola and Jones's seminal work [23]. The marriage of rectangular features and integral-imageenabled fast...
Ting Yu, Xiaoming Liu 0002