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» Learning Generative Models with the Up-Propagation Algorithm
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IROS
2009
IEEE
154views Robotics» more  IROS 2009»
14 years 2 months ago
Consideration on robotic giant-swing motion generated by reinforcement learning
—This study attempts to make a compact humanoid robot acquire a giant-swing motion without any robotic models by using reinforcement learning; only the interaction with environme...
Masayuki Hara, Naoto Kawabe, Naoki Sakai, Jian Hua...
ICRA
2010
IEEE
116views Robotics» more  ICRA 2010»
13 years 6 months ago
Parameterized maneuver learning for autonomous helicopter flight
Abstract— Many robotic control tasks involve complex dynamics that are hard to model. Hand-specifying trajectories that satisfy a system’s dynamics can be very time-consuming a...
Jie Tang, Arjun Singh, Nimbus Goehausen, Pieter Ab...
ACRI
2008
Springer
14 years 2 months ago
GP Generation of Pedestrian Behavioral Rules in an Evacuation Model Based on SCA
This paper presents a research in the context of pedestrian dynamics according to Situated Cellular Agent (SCA), a Multi-Agent Systems approach whose roots are on Cellular Automata...
Stefania Bandini, Sara Manzoni, Giancarlo Mauri, S...
ACL
2009
13 years 5 months ago
Learning with Annotation Noise
It is usually assumed that the kind of noise existing in annotated data is random classification noise. Yet there is evidence that differences between annotators are not always ra...
Eyal Beigman, Beata Beigman Klebanov
NIPS
2001
13 years 9 months ago
On the Generalization Ability of On-Line Learning Algorithms
In this paper, it is shown how to extract a hypothesis with small risk from the ensemble of hypotheses generated by an arbitrary on-line learning algorithm run on an independent an...
Nicolò Cesa-Bianchi, Alex Conconi, Claudio ...