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» Using output codes to boost multiclass learning problems
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ICML
2004
IEEE
14 years 4 months ago
Ensembles of nested dichotomies for multi-class problems
Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees ...
Eibe Frank, Stefan Kramer
ICML
2007
IEEE
14 years 11 months ago
Gradient boosting for kernelized output spaces
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...
ESANN
2007
14 years 10 days ago
Ensemble neural classifier design for face recognition
A method for tuning MLP learning parameters in an ensemble classifier framework is presented. No validation set or cross-validation technique is required to optimize parameters for...
Terry Windeatt
CVPR
2009
IEEE
15 years 6 months ago
Regularized Multi-Class Semi-Supervised Boosting
Many semi-supervised learning algorithms only deal with binary classification. Their extension to the multi-class problem is usually obtained by repeatedly solving a set of bina...
Amir Saffari, Christian Leistner, Horst Bischof
CIARP
2007
Springer
14 years 5 months ago
Multi-class Binary Object Categorization Using Blurred Shape Models
The main difficulty in the binary object classification field lays in dealing with a high variability of symbol appearance. Rotation, partial occlusions, elastic deformations, or...
Sergio Escalera, Alicia Fornés, Oriol Pujol...