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USENIX
1993
15 years 3 months ago
Fremont: A System for Discovering Network Characteristics and Problems
In this paper we present an architecture and prototype implementation for discovering key network characteristics, such as hosts, gateways, and topology. The Fremont system uses a...
David C. M. Wood, Sean S. Coleman, Michael F. Schw...
123
Voted
ML
2010
ACM
151views Machine Learning» more  ML 2010»
15 years 27 days ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
IJCNN
2006
IEEE
15 years 8 months ago
Global Reinforcement Learning in Neural Networks with Stochastic Synapses
— We have found a more general formulation of the REINFORCE learning principle which had been proposed by R. J. Williams for the case of artificial neural networks with stochast...
Xiaolong Ma, Konstantin Likharev
111
Voted
UAI
2004
15 years 3 months ago
"Ideal Parent" Structure Learning for Continuous Variable Networks
In recent years, there is a growing interest in learning Bayesian networks with continuous variables. Learning the structure of such networks is a computationally expensive proced...
Iftach Nachman, Gal Elidan, Nir Friedman
IFIP12
2004
15 years 3 months ago
Ensembles of Multi-Instance Neural Networks
: Recently, multi-instance classification algorithm BP-MIP and multi-instance regression algorithm BP-MIR both based on neural networks have been proposed. In this paper, neural ne...
Min-Ling Zhang, Zhi-Hua Zhou