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ICML
2009
IEEE
14 years 8 months ago
Unsupervised hierarchical modeling of locomotion styles
This paper describes an unsupervised learning technique for modeling human locomotion styles, such as distinct related activities (e.g. running and striding) or variations of the ...
Wei Pan, Lorenzo Torresani
TSP
2010
13 years 2 months ago
Learning graphical models for hypothesis testing and classification
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
JMLR
2006
125views more  JMLR 2006»
13 years 8 months ago
A Linear Non-Gaussian Acyclic Model for Causal Discovery
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data. Such methods make various assumptions on the data generating ...
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärin...
BMCBI
2010
229views more  BMCBI 2010»
13 years 8 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
NIPS
2001
13 years 9 months ago
On the Generalization Ability of On-Line Learning Algorithms
In this paper, it is shown how to extract a hypothesis with small risk from the ensemble of hypotheses generated by an arbitrary on-line learning algorithm run on an independent an...
Nicolò Cesa-Bianchi, Alex Conconi, Claudio ...