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» Feature Selection and Effective Classifiers
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PRIS
2004
13 years 10 months ago
Effect of Feature Smoothing Methods in Text Classification Tasks
Abstract. The number of features to be considered in a text classification system is given by the size of the vocabulary and this is normally in the range of the tens or hundreds o...
David Vilar, Hermann Ney, Alfons Juan, Enrique Vid...
ICMLA
2008
13 years 10 months ago
Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-f...
Jun Won Lee, Christophe G. Giraud-Carrier
ICFHR
2010
151views Biometrics» more  ICFHR 2010»
13 years 3 months ago
Error Reduction by Confusing Characters Discrimination for Online Handwritten Japanese Character Recognition
To reduce the classification errors of online handwritten Japanese character recognition, we propose a method for confusing characters discrimination with little additional costs....
Xiang-Dong Zhou, Da-Han Wang, Masaki Nakagawa, Che...
TNN
2008
124views more  TNN 2008»
13 years 8 months ago
Just-in-Time Adaptive Classifiers - Part II: Designing the Classifier
Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolu...
Cesare Alippi, Manuel Roveri
CIARP
2006
Springer
14 years 13 days ago
Feature Selection Based on Mutual Correlation
Feature selection is a critical procedure in many pattern recognition applications. There are two distinct mechanisms for feature selection namely the wrapper methods and the filte...
Michal Haindl, Petr Somol, Dimitrios Ververidis, C...