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BMCBI
2010
229views more  BMCBI 2010»
13 years 10 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
JSA
2000
175views more  JSA 2000»
13 years 10 months ago
Complete worst-case execution time analysis of straight-line hard real-time programs
In this article, the problem of finding a tight estimate on the worst-case execution time (WCET) of a real-time program is addressed. The analysis is focused on straight-line code...
Friedhelm Stappert, Peter Altenbernd
CIKM
2010
Springer
13 years 7 months ago
Regularization and feature selection for networked features
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
Hongliang Fei, Brian Quanz, Jun Huan
ICIP
2007
IEEE
14 years 12 months ago
Isomap Tracking with Particle Filtering
The problem of tracking involves challenges like in-plane and out-of-plane rotations, scaling, variations in ambient light and occlusions. In this paper we look at the problem of ...
Nikhil Rane, Stanley T. Birchfield
JMLR
2006
169views more  JMLR 2006»
13 years 10 months ago
Bayesian Network Learning with Parameter Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...