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» Dynamic Histograms: Capturing Evolving Data Sets
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UAI
2003
13 years 9 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller
KDD
2012
ACM
194views Data Mining» more  KDD 2012»
11 years 10 months ago
A sparsity-inducing formulation for evolutionary co-clustering
Traditional co-clustering methods identify block structures from static data matrices. However, the data matrices in many applications are dynamic; that is, they evolve smoothly o...
Shuiwang Ji, Wenlu Zhang, Jun Liu
3DPVT
2006
IEEE
197views Visualization» more  3DPVT 2006»
13 years 9 months ago
Structured Light Based Reconstruction under Local Spatial Coherence Assumption
3D scanning techniques based on structured light usually achieve robustness against outliers by performing multiple projections to simplify correspondence. However, for cases such...
Hao Li, Raphael Straub, Hartmut Prautzsch
SDM
2007
SIAM
96views Data Mining» more  SDM 2007»
13 years 9 months ago
Understanding and Utilizing the Hierarchy of Abnormal BGP Events
Abnormal events, such as security attacks, misconfigurations, or electricity failures, could have severe consequences toward the normal operation of the Border Gateway Protocol (...
Dejing Dou, Jun Li, Han Qin, Shiwoong Kim, Sheng Z...
INFSOF
2007
139views more  INFSOF 2007»
13 years 7 months ago
Predicting software defects in varying development lifecycles using Bayesian nets
An important decision problem in many software projects is when to stop testing and release software for use. For many software products, time to market is critical and therefore ...
Norman E. Fenton, Martin Neil, William Marsh, Pete...