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» Learning the Structure of Linear Latent Variable Models
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ICML
2006
IEEE
14 years 10 months ago
Full Bayesian network classifiers
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a BN, however, is typically of high computational complexity. In this paper, we e...
Jiang Su, Harry Zhang
KDD
2004
ACM
135views Data Mining» more  KDD 2004»
14 years 10 months ago
Discovering additive structure in black box functions
Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that driv...
Giles Hooker
GECCO
2005
Springer
232views Optimization» more  GECCO 2005»
14 years 3 months ago
Factorial representations to generate arbitrary search distributions
A powerful approach to search is to try to learn a distribution of good solutions (in particular of the dependencies between their variables) and use this distribution as a basis ...
Marc Toussaint
JMLR
2006
125views more  JMLR 2006»
13 years 10 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...
HICSS
2005
IEEE
159views Biometrics» more  HICSS 2005»
14 years 3 months ago
Knowledge Management Capability Assessment: Validating a Knowledge Assets Measurement Instrument
Measurement of organizational knowledge assets is necessary to determine the effectiveness of knowledge management initiatives. A Knowledge Management Capability Assessment instru...
Ron Freeze, Uday R. Kulkarni