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» Evaluating algorithms that learn from data streams
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ICPR
2008
IEEE
16 years 5 months ago
Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data
When a large amount of data are missing, or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. This paper presents a lea...
Qiang Ji, Wenhui Liao
EDBT
2011
ACM
225views Database» more  EDBT 2011»
14 years 8 months ago
SeMiTri: a framework for semantic annotation of heterogeneous trajectories
GPS devices allow recording the movement track of the moving object they are attached to. This data typically consists of a stream of spatio-temporal (x,y,t) points. For applicati...
Zhixian Yan, Dipanjan Chakraborty, Christine Paren...
VIS
2009
IEEE
399views Visualization» more  VIS 2009»
16 years 5 months ago
Visual Human+Machine Learning
In this paper we describe a novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user. The suggested approach combine...
Raphael Fuchs, Jürgen Waser, Meister Eduard GrÃ...
SIGOPS
2010
162views more  SIGOPS 2010»
15 years 2 months ago
Visual and algorithmic tooling for system trace analysis: a case study
Despite advances in the application of automated statistical and machine learning techniques to system log and trace data there will always be a need for human analysis of machine...
Wim De Pauw, Steve Heisig
TKDE
2010
393views more  TKDE 2010»
14 years 11 months ago
Adaptive Join Operators for Result Rate Optimization on Streaming Inputs
Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data is provided by autonomous data sources through heterogeneous network environ...
Mihaela A. Bornea, Vasilis Vassalos, Yannis Kotidi...