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ICCV
2005
IEEE
15 years 11 months ago
Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
In this paper, a new learning framework?probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the pro...
Zhuowen Tu
88
Voted
ICANN
2010
Springer
14 years 10 months ago
Exploring Continuous Action Spaces with Diffusion Trees for Reinforcement Learning
We propose a new approach for reinforcement learning in problems with continuous actions. Actions are sampled by means of a diffusion tree, which generates samples in the continuou...
Christian Vollmer, Erik Schaffernicht, Horst-Micha...
ICAART
2010
INSTICC
15 years 6 months ago
Complexity of Stochastic Branch and Bound Methods for Belief Tree Search in Bayesian Reinforcement Learning
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most...
Christos Dimitrakakis
ECML
2005
Springer
15 years 3 months ago
Learning (k, l)-Contextual Tree Languages for Information Extraction
This paper introduces a novel method for learning a wrapper for extraction of information from web pages, based upon (k,l)-contextual tree languages. It also introduces a method to...
Stefan Raeymaekers, Maurice Bruynooghe, Jan Van de...
ICASSP
2010
IEEE
14 years 10 months ago
Weakly supervised learning with decision trees applied to fisheries acoustics
This paper addresses the training of classification trees for weakly labelled data. We call ”weakly labelled data”, a training set such as the prior labelling information pro...
Riwal Lefort, Ronan Fablet, Jean-Marc Boucher