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» Learning Generative Models with the Up-Propagation Algorithm
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BMCBI
2006
101views more  BMCBI 2006»
13 years 5 months ago
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms
Background: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validatio...
Tim Van den Bulcke, Koen Van Leemput, Bart Naudts,...
HIS
2008
13 years 6 months ago
Artificial Data Sets Based on Knowledge Generators: Analysis of Learning Algorithms Efficiency
This paper proposes a methodology to generate artificial data sets to evaluate the behavior of machine learning techniques. The methodology relies in the definition of a domain an...
Joaquin Rios-Boutin, Albert Orriols-Puig, Josep Ma...
ICML
2010
IEEE
13 years 6 months ago
High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models
We develop a semi-supervised learning method that constrains the posterior distribution of latent variables under a generative model to satisfy a rich set of feature expectation c...
Gregory Druck, Andrew McCallum
ECML
2004
Springer
13 years 10 months ago
Model Approximation for HEXQ Hierarchical Reinforcement Learning
HEXQ is a reinforcement learning algorithm that discovers hierarchical structure automatically. The generated task hierarchy repthe problem at different levels of abstraction. In ...
Bernhard Hengst
PAMI
2007
185views more  PAMI 2007»
13 years 4 months ago
A Two-Level Generative Model for Cloth Representation and Shape from Shading
In this paper we present a two-level generative model for representing the images and surface depth maps of drapery and clothes. The upper level consists of a number of folds whic...
Feng Han, Song Chun Zhu