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Hannes Wettig

dblp:71/5737 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › parameter estimation
bayesian network parameter learning		 0.012003
When Discriminative Learning of Bayesian Network Parameters Is Easy · IJCAI 2003
YearPublicationVenuePosition
2005 On Discriminative Bayesian Network Classifiers and Logistic Regression
Teemu Roos, Hannes Wettig, Peter Grünwald, Petri Myllymäki, Henry Tirri
 Mach. Learn.2
2004 Topics in probabilistic location estimation in wireless networks
abstract
In this survey-style paper we demonstrate the usefulness of the probabilistic modelling framework in solving not only the actual positioning problem, but also many related problems involving issues like calibration, active learning, error estimation and tracking with history. We also point out some interesting links between positioning research done in the area of robotics and in the area of wireless radio networks.
Petri Kontkanen, Petri Myllymäki, Teemu Roos, Henry Tirri, Kimmo Valtonen, Hannes Wettig
 PIMRC6
2003 When Discriminative Learning of Bayesian Network Parameters Is Easy
Hannes Wettig, Peter Grünwald, Teemu Roos, Petri Myllymäki, Henry Tirri
 IJCAI1