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ECCV
2010
Springer
13 years 10 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 12 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
EDBT
2009
ACM
173views Database» more  EDBT 2009»
14 years 2 months ago
PROUD: a probabilistic approach to processing similarity queries over uncertain data streams
We present PROUD - A PRObabilistic approach to processing similarity queries over Uncertain Data streams, where the data streams here are mainly time series streams. In contrast t...
Mi-Yen Yeh, Kun-Lung Wu, Philip S. Yu, Ming-Syan C...
IROS
2009
IEEE
129views Robotics» more  IROS 2009»
14 years 4 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...
PODS
2006
ACM
95views Database» more  PODS 2006»
14 years 10 months ago
Randomized computations on large data sets: tight lower bounds
We study the randomized version of a computation model (introduced in [9, 10]) that restricts random access to external memory and internal memory space. Essentially, this model c...
André Hernich, Martin Grohe, Nicole Schweik...