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» Approximate Learning of Dynamic Models
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KDD
2007
ACM
152views Data Mining» more  KDD 2007»
16 years 5 months ago
Relational data pre-processing techniques for improved securities fraud detection
Commercial datasets are often large, relational, and dynamic. They contain many records of people, places, things, events and their interactions over time. Such datasets are rarel...
Andrew Fast, Lisa Friedland, Marc Maier, Brian Tay...
SIAMAM
2000
128views more  SIAMAM 2000»
15 years 4 months ago
Advection-Diffusion Equations for Internal State-Mediated Random Walks
Abstract. In many biological examples of biased random walks, movement statistics are determined by state dynamics that are internal to the organism or cell and that mediate respon...
Daniel Grünbaum
ML
2008
ACM
162views Machine Learning» more  ML 2008»
15 years 5 months ago
Incorporating prior knowledge in support vector regression
This paper explores the addition of constraints to the linear programming formulation of the support vector regression problem for the incorporation of prior knowledge. Equality an...
Fabien Lauer, Gérard Bloch
CVPR
2006
IEEE
16 years 7 months ago
Escaping local minima through hierarchical model selection: Automatic object discovery, segmentation, and tracking in video
Recently, the generative modeling approach to video segmentation has been gaining popularity in the computer vision community. For example, the flexible sprites framework has been...
Nebojsa Jojic, John M. Winn, Larry Zitnick
IJCNN
2006
IEEE
15 years 11 months ago
A Comparison between Recursive Neural Networks and Graph Neural Networks
— Recursive Neural Networks (RNNs) and Graph Neural Networks (GNNs) are two connectionist models that can directly process graphs. RNNs and GNNs exploit a similar processing fram...
Vincenzo Di Massa, Gabriele Monfardini, Lorenzo Sa...