Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Bayesian Averaging of Classifiers and the Overfitting Problem
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Normative Examination of Ensemble Learning Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
Machine learning in automated text categorisation
Machine learning in automated text categorisation
Learning a translation lexicon from monolingual corpora
ULA '02 Proceedings of the ACL-02 workshop on Unsupervised lexical acquisition - Volume 9
Filtering search results using an optimal set of terms identified by an artificial neural network
Information Processing and Management: an International Journal
Filtering search results using an optimal set of terms identified by an artificial neural network
Information Processing and Management: an International Journal
Class normalization in centroid-based text categorization
Information Sciences: an International Journal
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We improve a high-accuracy maximum entropy classifier by combining an ensemble of classifiers with neural network voting. In our experiments we demonstrate significantly superior performance both over a single classifier as well as over the use of the traditional weighted-sum voting approach. Specifically, we apply this to a maximum entropy classifier on a large scale multi-class text categorization task: the online job directory Flipdog with over half a million jobs in 65 categories.