The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Unifying the error-correcting and output-code AdaBoost within the margin framework
ICML '05 Proceedings of the 22nd international conference on Machine learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Boosting recombined weak classifiers
Pattern Recognition Letters
Augmented Display of Anatomical Names of Bronchial Branches for Bronchoscopy Assistance
MIAR '08 Proceedings of the 4th international workshop on Medical Imaging and Augmented Reality
Semi-Supervised Boosting for Multi-Class Classification
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Boosting One-Class Support Vector Machines for Multi-Class Classification
Applied Artificial Intelligence
An Information Theoretic Perspective on Multiple Classifier Systems
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Fusion of systems for automated cell phenotype image classification
Expert Systems with Applications: An International Journal
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
A new ensemble-based cascaded framework for multiclass training with simple weak learners
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Orientation invariant features for multiclass object recognition
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Improving Logitboost with prior knowledge
Information Fusion
Coarse-to-fine multiclass learning and classification for time-critical domains
Pattern Recognition Letters
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A multiclass classification problem can be reduced to a collection of binary problems with the aid of a coding matrix. The quality of the final solution, which is an ensemble of base classifiers learned on the binary problems, is affected by both the performance of the base learner and the error-correcting ability of the coding matrix. A coding matrix with strong error-correcting ability may not be overall optimal if the binary problems are too hard for the base learner. Thus a trade-off between error-correcting and base learning should be sought. In this paper, we propose a new multiclass boosting algorithm that modifies the coding matrix according to the learning ability of the base learner. We show experimentally that our algorithm is very efficient in optimizing the multiclass margin cost, and outperforms existing multiclass algorithms such as AdaBoost.ECC and one-vs-one. The improvement is especially significant when the base learner is not very powerful.