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» Learning the Relative Importance of Features in Image Data
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ICIP
2001
IEEE
16 years 1 months ago
Capturing image semantics with low-level descriptors
: We conducted psychophysical experiments to gain insight into the semantic categories that guide the human perception of image similarity. We analyzed the perceptual data using mu...
Aleksandra Mojsilovic, Bernice E. Rogowitz
ICIP
2007
IEEE
15 years 3 months ago
Unsupervised Nonlinear Manifold Learning
This communication deals with data reduction and regression. A set of high dimensional data (e.g., images) usually has only a few degrees of freedom with corresponding variables t...
Matthieu Brucher, Christian Heinrich, Fabrice Heit...
ICANN
2009
Springer
14 years 9 months ago
Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data
Different algorithms have been proposed in the literature to cluster gene expression data, however there is no single algorithm that can be considered the best one independently on...
André C. A. Nascimento, Ricardo Bastos Cava...
ICMCS
2009
IEEE
185views Multimedia» more  ICMCS 2009»
14 years 9 months ago
Connecting content to community in social media via image content, user tags and user communication
In this paper we develop a recommendation framework to connect image content with communities in online social media. The problem is important because users are looking for useful...
Munmun De Choudhury, Hari Sundaram, Yu-Ru Lin, Aji...
ML
2008
ACM
248views Machine Learning» more  ML 2008»
14 years 12 months ago
Feature selection via sensitivity analysis of SVM probabilistic outputs
Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) l...
Kai Quan Shen, Chong Jin Ong, Xiao Ping Li, Einar ...