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ICDAR
2003
IEEE
14 years 25 days ago
Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition
In this paper a methodology for feature selection in unsupervised learning is proposed. It makes use of a multiobjective genetic algorithm where the minimization of the number of ...
Marisa E. Morita, Robert Sabourin, Flávio B...
SDM
2010
SIAM
156views Data Mining» more  SDM 2010»
13 years 9 months ago
Co-selection of Features and Instances for Unsupervised Rare Category Analysis
Rare category analysis is of key importance both in theory and in practice. Previous research work focuses on supervised rare category analysis, such as rare category detection an...
Jingrui He, Jaime G. Carbonell
ICPR
2008
IEEE
14 years 2 months ago
Feature selection for clustering with constraints using Jensen-Shannon divergence
In semi-supervised clustering, domain knowledge can be converted to constraints and used to guide the clustering. In this paper we propose a feature selection algorithm for semi-s...
Yuanhong Li, Ming Dong, Yunqian Ma
CBMS
2006
IEEE
14 years 1 months ago
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
Inductive learning systems have been successfully applied in a number of medical domains. It is generally accepted that the highest accuracy results that an inductive learning sys...
Mykola Pechenizkiy, Alexey Tsymbal, Seppo Puuronen...
CVPR
2009
IEEE
15 years 29 days ago
Learning Signs from Subtitles: A Weakly Supervised Approach to Sign Language Recognition
This paper introduces a fully-automated, unsupervised method to recognise sign from subtitles. It does this by using data mining to align correspondences in sections of videos. Bas...
Helen Cooper, Richard Bowden