C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Artificial Intelligence Review - Special issue on lazy learning
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Probabilistic modeling for face orientation discrimination: learning from labeled and unlabeled data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Using unlabeled data to improve text classification
Using unlabeled data to improve text classification
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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We present a new algorithm called Ordered Classification, that is useful for classification problems where only few labeled examples are available but a large test set needs to be classified. In many real-world classification problems, it is expensive and some times unfeasible to acquire a large training set, thus, traditional supervised learning algorithms often perform poorly. In our algorithm, classification is performed by a discriminant approach similar to that of Query By Committee within the active learning setting. The method was applied to the real-world astronomical task of automated prediction of stellar atmospheric parameters, as well as to some benchmark learning problems showing a considerable improvement in classification accuracy over conventional algorithms.