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CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 5 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
CORR
2010
Springer
91views Education» more  CORR 2010»
13 years 4 months ago
Switching between Hidden Markov Models using Fixed Share
In prediction with expert advice the goal is to design online prediction algorithms that achieve small regret (additional loss on the whole data) compared to a reference scheme. I...
Wouter M. Koolen, Tim van Erven
AAAI
2007
14 years 18 min ago
Measuring the Level of Transfer Learning by an AP Physics Problem-Solver
Transfer learning is the ability of an agent to apply knowledge learned in previous tasks to new problems or domains. We approach this problem by focusing on model formulation, i....
Matthew Klenk, Kenneth D. Forbus
UAI
2007
13 years 10 months ago
Indirectly Visible Bayesian Student Models
Inspectable Bayesian student models have been used to support student reflection, knowledge awareness and communication among teacher, students and parents. This paper presents a...
Juan-Diego Zapata-Rivera
FGR
2000
IEEE
159views Biometrics» more  FGR 2000»
14 years 2 months ago
Gesture Modeling and Recognition Using Finite State Machines
This paper proposes a state based approach to gesture learning and recognition. Using spatial clustering and temporal alignment, each gesture is defined to be an ordered sequence ...
Pengyu Hong, Thomas S. Huang, Matthew Turk