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ICML
2006
IEEE
15 years 10 months ago
Efficient learning of Naive Bayes classifiers under class-conditional classification noise
We address the problem of efficiently learning Naive Bayes classifiers under classconditional classification noise (CCCN). Naive Bayes classifiers rely on the hypothesis that the ...
Christophe Nicolas Magnan, François Denis, ...
ICFCA
2007
Springer
15 years 3 months ago
A Parameterized Algorithm for Exploring Concept Lattices
Kuznetsov shows that Formal Concept Analysis (FCA) is a natural framework for learning from positive and negative examples. Indeed, the results of learning from positive examples (...
Peggy Cellier, Sébastien Ferré, Oliv...
SDM
2007
SIAM
182views Data Mining» more  SDM 2007»
14 years 11 months ago
Distance Preserving Dimension Reduction for Manifold Learning
Manifold learning is an effective methodology for extracting nonlinear structures from high-dimensional data with many applications in image analysis, computer vision, text data a...
Hyunsoo Kim, Haesun Park, Hongyuan Zha
GECCO
2003
Springer
160views Optimization» more  GECCO 2003»
15 years 3 months ago
Using Genetic Algorithms for Data Mining Optimization in an Educational Web-Based System
This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A comb...
Behrouz Minaei-Bidgoli, William F. Punch
ML
1998
ACM
153views Machine Learning» more  ML 1998»
14 years 9 months ago
Bayesian Landmark Learning for Mobile Robot Localization
To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optim...
Sebastian Thrun