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JMLR
2010
140views more  JMLR 2010»
13 years 2 months ago
Learning Non-Stationary Dynamic Bayesian Networks
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
Joshua W. Robinson, Alexander J. Hartemink
COLT
1993
Springer
13 years 11 months ago
Lower Bounds on the Vapnik-Chervonenkis Dimension of Multi-Layer Threshold Networks
We consider the problem of learning in multilayer feed-forward networks of linear threshold units. We show that the Vapnik-Chervonenkis dimension of the class of functions that ca...
Peter L. Bartlett
MVA
1990
101views Computer Vision» more  MVA 1990»
13 years 8 months ago
Recognition of Parametrised Models from 3D Data
This paper describes work done as part of the Oxford AGV (Autonomous Guided Vehicle) project [2] towards recognition of classes of objects to be encountered in a factory environme...
Ian D. Reid
TSMC
1998
91views more  TSMC 1998»
13 years 7 months ago
Toward the border between neural and Markovian paradigms
— A new tendency in the design of modern signal processing methods is the creation of hybrid algorithms. This paper gives an overview of different signal processing algorithms si...
Piotr Wilinski, Basel Solaiman, A. Hillion, W. Cza...
LCN
1998
IEEE
13 years 12 months ago
High Performance Integrated Network Communications Architecture (INCA)
Current communication subsystem mechanisms within workstation and PC class computers are limiting network communications throughput to a small percentage of the present network da...
Klaus Schug, Anura P. Jayasumana, Prasanth Gopalak...