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ECCV
2010
Springer
13 years 8 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
LATIN
2004
Springer
14 years 1 months ago
Generating Maximal Independent Sets for Hypergraphs with Bounded Edge-Intersections
Given a finite set V , and integers k ≥ 1 and r ≥ 0, denote by A(k, r) the class of hypergraphs A ⊆ 2V with (k, r)-bounded intersections, i.e. in which the intersection of a...
Endre Boros, Khaled M. Elbassioni, Vladimir Gurvic...
AAAI
2008
13 years 11 months ago
Resource Constrained Distributed Constraint Optimization with Virtual Variables
Cooperative problem solving with resource constraints are important in practical multi-agent systems. Resource constraints are necessary to handle practical problems including dis...
Toshihiro Matsui, Hiroshi Matsuo, Marius Silaghi, ...
BIBE
2008
IEEE
142views Bioinformatics» more  BIBE 2008»
14 years 3 months ago
Optimizing performance, cost, and sensitivity in pairwise sequence search on a cluster of PlayStations
— The Smith-Waterman algorithm is a dynamic programming method for determining optimal local alignments between nucleotide or protein sequences. However, it suffers from quadrati...
Ashwin M. Aji, Wu-chun Feng
COCO
2005
Springer
123views Algorithms» more  COCO 2005»
14 years 2 months ago
If NP Languages are Hard on the Worst-Case Then It is Easy to Find Their Hard Instances
We prove that if NP ⊆ BPP, i.e., if SAT is worst-case hard, then for every probabilistic polynomial-time algorithm trying to decide SAT, there exists some polynomially samplable ...
Dan Gutfreund, Ronen Shaltiel, Amnon Ta-Shma