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GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
15 years 1 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
131
Voted
ICCV
2009
IEEE
15 years 1 months ago
Associative hierarchical CRFs for object class image segmentation
Most methods for object class segmentation are formulated as a labelling problem over a single choice of quantisation of an image space - pixels, segments or group of segments. It...
Lubor Ladicky, Christopher Russell, Pushmeet Kohli...
179
Voted
CVPR
2011
IEEE
14 years 12 months ago
Global Stereo Matching Leveraged by Sparse Ground Control Points
We present a novel global stereo model that makes use of constraints from points with known depths, i.e., the Ground Control Points (GCPs) as referred to in stereo literature. Our...
Liang Wang, Ruigang Yang
ICPR
2008
IEEE
16 years 5 months ago
A new objective function for sequence labeling
We propose a new loss function for discriminative learning of Markov random fields, which is an intermediate loss function between the sequential loss and the pointwise loss. We s...
Hisashi Kashima, Yuta Tsuboi
KDD
2009
ACM
173views Data Mining» more  KDD 2009»
16 years 4 months ago
The offset tree for learning with partial labels
We present an algorithm, called the offset tree, for learning in situations where a loss associated with different decisions is not known, but was randomly probed. The algorithm i...
Alina Beygelzimer, John Langford