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EMNLP
2010
14 years 7 months ago
Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models
We describe a new scalable algorithm for semi-supervised training of conditional random fields (CRF) and its application to partof-speech (POS) tagging. The algorithm uses a simil...
Amarnag Subramanya, Slav Petrov, Fernando Pereira
CVPR
2009
IEEE
15 years 1 months ago
Imbalanced RankBoost for efficiently ranking large-scale image/video collections
Ranking large scale image and video collections usually expects higher accuracy on top ranked data, while tolerates lower accuracy on bottom ranked ones. In view of this, we propo...
Michele Merler, Rong Yan, John R. Smith
CLOUDCOM
2010
Springer
14 years 7 months ago
Scaling Populations of a Genetic Algorithm for Job Shop Scheduling Problems Using MapReduce
Inspired by Darwinian evolution, a genetic algorithm (GA) approach is one of the popular heuristic methods for solving hard problems, such as the Job Shop Scheduling Problem (JSSP...
Di-Wei Huang, Jimmy Lin
ECOOP
2009
Springer
15 years 10 months ago
Scaling CFL-Reachability-Based Points-To Analysis Using Context-Sensitive Must-Not-Alias Analysis
Pointer analyses derived from a Context-Free-Language (CFL) reachability formulation achieve very high precision, but they do not scale well to compute the points-to solution for a...
Guoqing Xu, Atanas Rountev, Manu Sridharan
ICCV
2007
IEEE
15 years 11 months ago
Capacity Scaling for Graph Cuts in Vision
Capacity scaling is a hierarchical approach to graph representation that can improve theoretical complexity and practical efficiency of max-flow/min-cut algorithms. Introduced by ...
Olivier Juan, Yuri Boykov