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» Using model knowledge for learning inverse dynamics
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144
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ICCV
2003
IEEE
15 years 8 months ago
Reinforcement Learning for Combining Relevance Feedback Techniques
Relevance feedback (RF) is an interactive process which refines the retrievals by utilizing user’s feedback history. Most researchers strive to develop new RF techniques and ign...
Peng-Yeng Yin, Bir Bhanu, Kuang-Cheng Chang, Anlei...
123
Voted
AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
15 years 8 months ago
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Prefer...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh
101
Voted
ICMCS
2006
IEEE
129views Multimedia» more  ICMCS 2006»
15 years 8 months ago
Modeling Interactions from Email Communication
Email plays an important role as a medium for the spread of information, ideas, and influence among its users. We present a framework to learn topic-based interactions between pa...
Dong Zhang, Daniel Gatica-Perez, Deb Roy, Samy Ben...
126
Voted
EXPERT
2010
145views more  EXPERT 2010»
15 years 14 hour ago
Interaction Analysis with a Bayesian Trajectory Model
Human behavior recognition is one of the most important and challenging objectives performed by intelligent vision systems. Several issues must be faced in this domain ranging fro...
Alessio Dore, Carlo S. Regazzoni
131
Voted
BMCBI
2008
174views more  BMCBI 2008»
15 years 2 months ago
Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset
Background: Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interact...
Cédric Auliac, Vincent Frouin, Xavier Gidro...