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
Optimal linear combinations of neural networks
Neural Networks
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
On the Decomposition of Polychotomies into Dichotomies
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Parallel Non Linear Dichotomizers
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Dependence among Codeword Bits Errors in ECOC Learning Machines: An Experimental Analysis
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Least Squares and Estimation Measures via Error Correcting Output Code
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Using diversity measures for generating error-correcting output codes in classifier ensembles
Pattern Recognition Letters
On ECOC as binary ensemble classifiers
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Artificial Intelligence in Medicine
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In the framework of decomposition methods for multiclass classification problems, error correcting output codes (ECOC) can be fruitfully used as codewords for coding classes in order to enhance the generalization capability of learning machines. The effectiveness of error correcting output codes depends mainly on the independence of codeword bits and on the accuracy by which each dichotomy is learned. Separated and non-linear dichotomizers can improve the independence among computed codeword bits, thus fully exploiting the error recovering capabilities of ECOC. In the experimentation presented in this paper we compare ECOC decomposition methods implemented through monolithic multi-layer perceptrons and sets of linear and non-linear independent dichotomizers. The most effectiveness of ECOC decomposition scheme is obtained by Parallel Non-linear Dichotomizers (PND), a learning machine based on decomposition of polychotomies into dichotomies, using non linear independent dichotomizers.