C4.5: programs for machine learning
C4.5: programs for machine learning
On-line prediction and conversion strategies
Machine Learning
Combining Error-Driven Pruning and Classification for Partial Parsing
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Data-oriented methods for grapheme-to-phoneme conversion
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Part-of-speech tagging with neural networks
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
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Error-correcting output codes (ECOC) have emerged in machine learning as a successful implementation of the idea of distributed classes. Monadic class symbols are replaced by bit strings, which are learned by an ensemble of binary-valued classifiers (dichotomizers). In this study, the idea of ECOC is applied to memory-based language learning with local (k-nearest neighbor) classifiers. Regression analysis of the experimental results reveals that, in order for ECOC to be successful for language learning, the use of the Modified Value Difference Metric (MVDM) is an important factor, which is explained in terms of population density of the class hyperspace.