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NIPS
2008
13 years 11 months ago
Hebbian Learning of Bayes Optimal Decisions
Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it. Mathematical models fo...
Bernhard Nessler, Michael Pfeiffer, Wolfgang Maass
PAKDD
2000
ACM
161views Data Mining» more  PAKDD 2000»
14 years 1 months ago
Adaptive Boosting for Spatial Functions with Unstable Driving Attributes
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic
CVIU
2010
115views more  CVIU 2010»
13 years 10 months ago
A modified model for the Lobula Giant Movement Detector and its FPGA implementation
Bio-inspired vision sensors are particularly appropriate candidates for navigation of vehicles or mobile robots due to their computational simplicity, allowing compact hardware im...
Hongying Meng, Kofi Appiah, Shigang Yue, Andrew Hu...
ICANN
2007
Springer
14 years 4 months ago
Structure Learning with Nonparametric Decomposable Models
Abstract. We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete a...
Anton Schwaighofer, Mathäus Dejori, Volker Tr...
ANOR
2010
139views more  ANOR 2010»
13 years 8 months ago
Some operations research methods for analyzing protein sequences and structures
Operations Research is probably one of the most successful fields of applied mathematics used in Economics, Physics, Chemistry, almost everywhere onehastoanalyzehugeamountsofdata.L...
Piotr Lukasiak, Jacek Blazewicz, Maciej Milostan