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ECCV
2000
Springer
14 years 10 months ago
Unsupervised Learning of Models for Recognition
We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of mode...
Markus Weber, Max Welling, Pietro Perona
DIS
2007
Springer
14 years 2 months ago
Fast NML Computation for Naive Bayes Models
Abstract. The Minimum Description Length (MDL) is an informationtheoretic principle that can be used for model selection and other statistical inference tasks. One way to implement...
Tommi Mononen, Petri Myllymäki
CSCW
2004
ACM
14 years 1 months ago
BusyBody: creating and fielding personalized models of the cost of interruption
Interest has been growing in opportunities to build and deploy statistical models that can infer a computer user’s current interruptability from computer activity and relevant c...
Eric Horvitz, Paul Koch, Johnson Apacible
ESSMAC
2003
Springer
14 years 1 months ago
Self-tuning Control of Non-linear Systems Using Gaussian Process Prior Models
Gaussian Process prior models, as used in Bayesian non-parametric statistical models methodology are applied to implement a nonlinear adaptive control law. The expected value of a...
Daniel Sbarbaro, Roderick Murray-Smith
NECO
1998
119views more  NECO 1998»
13 years 7 months ago
Density Estimation by Mixture Models with Smoothing Priors
In the statistical approach for self-organizing maps (SOMs), learning is regarded as an estimation algorithm for a Gaussian mixture model with a Gaussian smoothing prior on the ce...
Akio Utsugi