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CORR
2007
Springer
73views Education» more  CORR 2007»
13 years 7 months ago
Universal Reinforcement Learning
—We consider an agent interacting with an unmodeled environment. At each time, the agent makes an observation, takes an action, and incurs a cost. Its actions can influence futu...
Vivek F. Farias, Ciamac Cyrus Moallemi, Tsachy Wei...
ICASSP
2008
IEEE
14 years 1 months ago
Compressed sensing with sequential observations
Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of measurements. The results in the literature have focuse...
Dmitry M. Malioutov, Sujay Sanghavi, Alan S. Wills...
AMR
2003
Springer
179views Multimedia» more  AMR 2003»
14 years 18 days ago
Building User Models from Observations of Users Accessing Multimedia Learning Objects
Abstract. We report our work towards building user models of learner’s development based upon evidence of their interactions with an e-learning website composed of multimedia lea...
Judy Kay, Andrew Lum
IJCAI
2007
13 years 8 months ago
Learning from Partial Observations
We present a general machine learning framework for modelling the phenomenon of missing information in data. We propose a masking process model to capture the stochastic nature of...
Loizos Michael
ECAI
2004
Springer
14 years 23 days ago
Combining Multiple Answers for Learning Mathematical Structures from Visual Observation
Learning general truths from the observation of simple domains and, further, learning how to use this knowledge are essential capabilities for any intelligent agent to understand ...
Paulo Santos, Derek R. Magee, Anthony G. Cohn, Dav...