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ICA
2010
Springer
13 years 9 months ago
Use of Prior Knowledge in a Non-Gaussian Method for Learning Linear Structural Equation Models
Abstract. We discuss causal structure learning based on linear structural equation models. Conventional learning methods most often assume Gaussianity and create many indistinguish...
Takanori Inazumi, Shohei Shimizu, Takashi Washio
TIP
2002
179views more  TIP 2002»
13 years 7 months ago
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of i...
Te-Won Lee, Michael S. Lewicki
UAI
2008
13 years 9 months ago
Cumulative distribution networks and the derivative-sum-product algorithm
We introduce a new type of graphical model called a `cumulative distribution network' (CDN), which expresses a joint cumulative distribution as a product of local functions. ...
Jim C. Huang, Brendan J. Frey
ICML
2005
IEEE
14 years 8 months ago
Learning first-order probabilistic models with combining rules
Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models de...
Sriraam Natarajan, Prasad Tadepalli, Eric Altendor...
ICAI
2004
13 years 9 months ago
A Comparison of Resampling Methods for Clustering Ensembles
-- Combination of multiple clusterings is an important task in the area of unsupervised learning. Inspired by the success of supervised bagging algorithms, we propose a resampling ...
Behrouz Minaei-Bidgoli, Alexander P. Topchy, Willi...