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» Calibrating Random Forests
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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
APPROX
2008
Springer
184views Algorithms» more  APPROX 2008»
13 years 10 months ago
Approximately Counting Embeddings into Random Graphs
Let H be a graph, and let CH(G) be the number of (subgraph isomorphic) copies of H contained in a graph G. We investigate the fundamental problem of estimating CH(G). Previous res...
Martin Fürer, Shiva Prasad Kasiviswanathan
IROS
2009
IEEE
129views Robotics» more  IROS 2009»
14 years 3 months ago
Planning fireworks trajectories for steerable medical needles to reduce patient trauma
— Accurate needle insertion in 3D environment is always a grand challenge. When multiple targets are located in the tissue, a procedure of inserting multiple needles from a singl...
Jijie Xu, Vincent Duindam, Ron Alterovitz, Jean Po...
SENSYS
2010
ACM
13 years 6 months ago
Locating sensors in the wild: pursuit of ranging quality
Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. The real-world experience from GreenOrbs, a sensor network sys...
Wei Xi, Yuan He, Yunhao Liu, Jizhong Zhao, Lufeng ...
ICIP
2010
IEEE
13 years 6 months ago
Randomly driven fuzzy key extraction of unclonable images
In this paper, we develop an adjustable Fuzzy Extractor using the Physical Unclonable Functions (PUF) obtained by a common laser engraving method to sign physical objects. In part...
Saloomeh Shariati, Laurent Jacques, Françoi...