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» Using Learning for Approximation in Stochastic Processes
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SCALESPACE
2009
Springer
14 years 3 months ago
Line Enhancement and Completion via Linear Left Invariant Scale Spaces on SE(2)
From an image we construct an invertible orientation score, which provides an overview of local orientations in an image. This orientation score is a function on the group SE(2) of...
Remco Duits, Erik Franken
ICML
2005
IEEE
14 years 9 months ago
Bayesian hierarchical clustering
We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages ove...
Katherine A. Heller, Zoubin Ghahramani
RSS
2007
176views Robotics» more  RSS 2007»
13 years 10 months ago
Active Policy Learning for Robot Planning and Exploration under Uncertainty
Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
Ruben Martinez-Cantin, Nando de Freitas, Arnaud Do...
ICML
2010
IEEE
13 years 9 months ago
Convergence of Least Squares Temporal Difference Methods Under General Conditions
We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) in the off-policy learning context and with the simulation-based least square...
Huizhen Yu
FORTE
2008
13 years 10 months ago
Detecting Communication Protocol Security Flaws by Formal Fuzz Testing and Machine Learning
Network-based fuzz testing has become an effective mechanism to ensure the security and reliability of communication protocol systems. However, fuzz testing is still conducted in a...
Guoqiang Shu, Yating Hsu, David Lee