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» Learning the Relative Importance of Features in Image Data
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JMLR
2012
11 years 11 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee
ICCV
2011
IEEE
12 years 8 months ago
Learning Cross-modality Similarity for Multinomial Data
Many applications involve multiple-modalities such as text and images that describe the problem of interest. In order to leverage the information present in all the modalities, on...
Yangqing Jia, Mathieu Salzmann, Trevor Darrell
BMEI
2008
IEEE
13 years 10 months ago
Clustering of High-Dimensional Gene Expression Data with Feature Filtering Methods and Diffusion Maps
The importance of gene expression data in cancer diagnosis and treatment by now has been widely recognized by cancer researchers in recent years. However, one of the major challen...
Rui Xu, Steven Damelin, Boaz Nadler, Donald C. Wun...
ICPR
2004
IEEE
14 years 9 months ago
Learning High-level Independent Components of Images through a Spectral Representation
Statistical methods, such as independent component analysis, have been successful in learning local low-level features from natural image data. Here we extend these methods for le...
Aapo Hyvärinen, Jussi T. Lindgren
KESAMSTA
2010
Springer
14 years 1 months ago
Classifying Agent Behaviour through Relational Sequential Patterns
Abstract. In Multi-Agent System, observing other agents and modelling their behaviour represents an essential task: agents must be able to quickly adapt to the environment and infe...
Grazia Bombini, Nicola Di Mauro, Stefano Ferilli, ...