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SAGT
2009
Springer
192views Game Theory» more  SAGT 2009»
15 years 8 months ago
Learning and Approximating the Optimal Strategy to Commit To
Computing optimal Stackelberg strategies in general two-player Bayesian games (not to be confused with Stackelberg strategies in routing games) is a topic that has recently been ga...
Joshua Letchford, Vincent Conitzer, Kamesh Munagal...
IROS
2007
IEEE
123views Robotics» more  IROS 2007»
15 years 8 months ago
Reinforcement learning in multi-dimensional state-action space using random rectangular coarse coding and Gibbs sampling
: This paper presents a coarse coding technique and an action selection scheme for reinforcement learning (RL) in multi-dimensional and continuous state-action spaces following con...
Kimura Kimura
IDA
2007
Springer
15 years 8 months ago
Learning to Align: A Statistical Approach
We present a new machine learning approach to the inverse parametric sequence alignment problem: given as training examples a set of correct pairwise global alignments, find the p...
Elisa Ricci, Tijl De Bie, Nello Cristianini
TAICPART
2006
IEEE
134views Education» more  TAICPART 2006»
15 years 7 months ago
Integration Testing of Components Guided by Incremental State Machine Learning
The design of complex systems, e.g., telecom services, is nowadays usually based on the integration of components (COTS), loosely coupled in distributed architectures. When compon...
Keqin Li 0002, Roland Groz, Muzammil Shahbaz
101
Voted
ICCV
2005
IEEE
15 years 7 months ago
Learning Hierarchical Models of Scenes, Objects, and Parts
We describe a hierarchical probabilistic model for the detection and recognition of objects in cluttered, natural scenes. The model is based on a set of parts which describe the e...
Erik B. Sudderth, Antonio B. Torralba, William T. ...