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NIPS
2008
14 years 11 months ago
Stochastic Relational Models for Large-scale Dyadic Data using MCMC
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
Shenghuo Zhu, Kai Yu, Yihong Gong
IJHIS
2006
94views more  IJHIS 2006»
14 years 9 months ago
A new fine-grained evolutionary algorithm based on cellular learning automata
In this paper, a new evolutionary computing model, called CLA-EC, is proposed. This model is a combination of a model called cellular learning automata (CLA) and the evolutionary ...
Reza Rastegar, Mohammad Reza Meybodi, Arash Hariri
IIE
2007
63views more  IIE 2007»
14 years 9 months ago
Investigation of Q-Learning in the Context of a Virtual Learning Environment
We investigate the possibility to apply a known machine learning algorithm of Q-learning in the domain of a Virtual Learning Environment (VLE). It is important in this problem doma...
Dalia Baziukaite
AVSS
2006
IEEE
15 years 3 months ago
Learning Foveal Sensing Strategies in Unconstrained Surveillance Environments
In this paper we report on techniques for automatically learning foveal sensing strategies for an active pan-tiltzoom camera. The approach uses reinforcement learning to discover ...
Andrew D. Bagdanov, Alberto Del Bimbo, Walter Nunz...
ICASSP
2008
IEEE
15 years 4 months ago
Using dialogue acts to learn better repair strategies for spoken dialogue systems
Repair or error-recovery strategies are an important design issue in Spoken Dialogue Systems (SDSs) - how to conduct the dialogue when there is no progress (e.g. due to repeated A...
Matthew Frampton, Oliver Lemon