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» Approximate Learning of Dynamic Models
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ICDM
2009
IEEE
155views Data Mining» more  ICDM 2009»
16 years 1 months ago
Stacked Gaussian Process Learning
—Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utili...
Marion Neumann, Kristian Kersting, Zhao Xu, Daniel...
JMLR
2010
137views more  JMLR 2010»
15 years 1 months ago
Importance Sampling for Continuous Time Bayesian Networks
A continuous time Bayesian network (CTBN) uses a structured representation to describe a dynamic system with a finite number of states which evolves in continuous time. Exact infe...
Yu Fan, Jing Xu, Christian R. Shelton
140
Voted
ICML
2005
IEEE
16 years 7 months ago
Reducing overfitting in process model induction
In this paper, we review the paradigm of inductive process modeling, which uses background knowledge about possible component processes to construct quantitative models of dynamic...
Will Bridewell, Narges Bani Asadi, Pat Langley, Lj...
WSC
2000
15 years 7 months ago
Teaching system modeling, simulation and validation
Simulation is used in the design process of dynamic systems. The results of simulation are employed for validating a model, and they are helpful for the improvement of the design ...
Jörg Desel
JMLR
2008
92views more  JMLR 2008»
15 years 6 months ago
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
This paper presents the dynamics of multiple learning agents from an evolutionary game theoretic perspective. We provide replicator dynamics models for cooperative coevolutionary ...
Liviu Panait, Karl Tuyls, Sean Luke