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» Learning Markov Network Structure with Decision Trees
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ICANN
2010
Springer
13 years 10 months ago
Unsupervised Learning of Relations
Learning processes allow the central nervous system to learn relationships between stimuli. Even stimuli from different modalities can easily be associated, and these associations ...
Matthew Cook, Florian Jug, Christoph Krautz, Angel...
JSAC
2011
83views more  JSAC 2011»
12 years 11 months ago
Trading Rate for Balanced Queue Lengths for Network Delay Minimization
—We consider a communication channel with two transmitters and one receiver, with an underlying rate region which is approximated as a general pentagon. Different from the Gaussi...
Jing Yang, Sennur Ulukus
IADIS
2004
13 years 10 months ago
Electronic case studies: a problem-based learning approach
E-Cases is an innovative approach to management development. Traditional case studies typically describe a decision or a problem in a real-life setting. E-Cases encourage students...
Philip M. Drinkwater, Christopher P. Holland, K. N...
DSN
2005
IEEE
14 years 2 months ago
A Wavefront Parallelisation of CTMC Solution Using MTBDDs
In this paper, we present a parallel implementation for the steady-state analysis of continuous-time Markov chains (CTMCs). This analysis is performed via solution of a linear equ...
Yi Zhang, David Parker, Marta Z. Kwiatkowska
NIPS
1990
13 years 10 months ago
Bumptrees for Efficient Function, Constraint and Classification Learning
A new class of data structures called "bumptrees" is described. These structures are useful for efficiently implementing a number of neural network related operations. A...
Stephen M. Omohundro