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» Learning about and through Empirical Modelling
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MLG
2007
Springer
14 years 2 months ago
Inferring Vertex Properties from Topology in Large Networks
: Network topology not only tells about tightly-connected “communities,” but also gives cues on more subtle properties of the vertices. We introduce a simple probabilistic late...
Janne Sinkkonen, Janne Aukia, Samuel Kaski
JAIR
1998
198views more  JAIR 1998»
13 years 8 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Alberto Ruiz, Pedro E. López-de-Teruel, M. ...
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
14 years 1 months ago
Grouped graphical Granger modeling methods for temporal causal modeling
We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
ICRA
2002
IEEE
114views Robotics» more  ICRA 2002»
14 years 1 months ago
Learning Motion Patterns of Persons for Mobile Service Robots
We propose a method for learning models of people’s motion behaviors in an indoor environment. As people move through their environments, they do not move randomly. Instead, the...
Maren Bennewitz, Wolfram Burgard, Sebastian Thrun
PST
2004
13 years 9 months ago
Supporting Privacy in E-Learning with Semantic Streams
The goal of the semantic web is to facilitate the exchange of meaningful information in a form that is easy for machines to process. The goal of an e-learning system is to support ...
Lori Kettel, Christopher A. Brooks, Jim E. Greer