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ICML
2004
IEEE
14 years 8 months ago
Learning Bayesian network classifiers by maximizing conditional likelihood
Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when lear...
Daniel Grossman, Pedro Domingos
ICASSP
2008
IEEE
14 years 2 months ago
Distributed Kalman filtering based on quantized innovations
We consider state estimation of a Markov stochastic process using an ad hoc wireless sensor network (WSN) based on noisy linear observations. Due to power and bandwidth constraint...
Eric J. Msechu, Alejandro Ribeiro, Stergios I. Rou...
ICPR
2000
IEEE
13 years 12 months ago
Model-Based Halftoning for Color Image Segmentation
Grouping algorithms based on histograms over measured image features have very successfully been applied to textured image segmentation [2, 11, 6]. However, the competing goals of...
Jan Puzicha, Serge Belongie
DAC
1996
ACM
13 years 11 months ago
Use of Sensitivities and Generalized Substrate Models in Mixed-Signal IC Design
A novel methodology for circuit design and automatic layout generation is proposed for a class of mixed-signal circuits in presence of layout parasitics and substrate induced nois...
Paolo Miliozzi, Iasson Vassiliou, Edoardo Charbon,...
NIPS
2003
13 years 9 months ago
Ambiguous Model Learning Made Unambiguous with 1/f Priors
What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the a...
Gurinder S. Atwal, William Bialek