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UAI
1996
15 years 5 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
CVPR
2008
IEEE
16 years 6 months ago
Semi-supervised learning of multi-factor models for face de-identification
With the emergence of new applications centered around the sharing of image data, questions concerning the protection of the privacy of people visible in the scene arise. Recently...
Ralph Gross, Latanya Sweeney, Fernando De la Torre...
138
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ACL
2010
15 years 2 months ago
Phrase-Based Statistical Language Generation Using Graphical Models and Active Learning
Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to i...
François Mairesse, Milica Gasic, Filip Jurc...
AUSAI
2006
Springer
15 years 7 months ago
Learning Hybrid Bayesian Networks by MML
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditi...
Rodney T. O'Donnell, Lloyd Allison, Kevin B. Korb
ALT
1998
Springer
15 years 8 months ago
Predictive Learning Models for Concept Drift
Concept drift means that the concept about which data is obtained may shift from time to time, each time after some minimum permanence. Except for this minimum permanence, the con...
John Case, Sanjay Jain, Susanne Kaufmann, Arun Sha...