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» Bayesian learning of measurement and structural models
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ICDM
2008
IEEE
156views Data Mining» more  ICDM 2008»
15 years 6 months ago
Exploiting Local and Global Invariants for the Management of Large Scale Information Systems
This paper presents a data oriented approach to modeling the complex computing systems, in which an ensemble of correlation models are discovered to represent the system status. I...
Haifeng Chen, Haibin Cheng, Guofei Jiang, Kenji Yo...
BMCBI
2006
119views more  BMCBI 2006»
14 years 12 months ago
Hidden Markov Model Variants and their Application
Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way...
Stephen Winters-Hilt
HICSS
2005
IEEE
160views Biometrics» more  HICSS 2005»
15 years 5 months ago
Using Content and Process Scaffolds to Support Collaborative Discourse in Asynchronous Learning Networks
Discourse, a form of collaborative learning [44], is one of the most widely used methods of teaching and learning in the online environment. Particularly in large courses, discour...
I. Wong-Bushby, Starr Roxanne Hiltz, Michael Biebe...
ECCV
2002
Springer
16 years 1 months ago
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
Abstract. This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an ...
Hedvig Sidenbladh, Michael J. Black, Leonid Sigal
ICML
2004
IEEE
16 years 21 days ago
Parameter space exploration with Gaussian process trees
Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response. Such sweeps can be prohibitively expensive, and are ...
Robert B. Gramacy, Herbert K. H. Lee, William G. M...