Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
C4.5: programs for machine learning
C4.5: programs for machine learning
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
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
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
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This paper presents a novel framework of error-correcting output coding (ECOC) addressing the problem of multi-class classification. By weighting the output space of each base classifier which is trained independently, the distance function of decoding is adapted so that the samples are more discriminative. A criterion generated over the Extended Pair Samples (EPS) is proposed to train the weights of output space. Some properties still hold in the new framework: any classifier, as well as distance function, is still applicable. We first conduct empirical studies on UCI datasets to verify the presented framework with four frequently used coding matrixes and then apply it in RoboCup domain to enhance the performance of agent control. Experimental results show that our supervised learned decoding scheme improves the accuracy of classification significantly and betters the ball control of agents in a soccer game after learning from experience.