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NIPS
2000
15 years 5 months ago
Discovering Hidden Variables: A Structure-Based Approach
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. As s...
Gal Elidan, Noam Lotner, Nir Friedman, Daphne Koll...
UAI
2000
15 years 5 months ago
Gaussian Process Networks
In this paper we address the problem of learning the structure of a Bayesian network in domains with continuous variables. This task requires a procedure for comparing different c...
Nir Friedman, Iftach Nachman
ICML
2010
IEEE
15 years 5 months ago
From Transformation-Based Dimensionality Reduction to Feature Selection
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy
IVC
2008
182views more  IVC 2008»
15 years 4 months ago
Ontology based complex object recognition
This paper presents an object categorization method. Our approach involves the following aspects of cognitive vision : machine learning and knowledge representation. A major eleme...
Nicolas Maillot, Monique Thonnat
IJCINI
2007
106views more  IJCINI 2007»
15 years 4 months ago
The OAR Model of Neural Informatics for Internal Knowledge Representation in the Brain
The cognitive models of information representation are fundamental research areas in cognitive informatics, which attempts to reveal the mechanisms and potential of the brain in l...
Yingxu Wang