Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
ACM SIGKDD Explorations Newsletter
Propositionalization approaches to relational data mining
Relational Data Mining
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Transformation-Based Learning Using Multirelational Aggregation
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On semi-supervised clustering via multiobjective optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Analyzing Co-training Style Algorithms
ECML '07 Proceedings of the 18th European conference on Machine Learning
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We focus on the problem of predicting functional propertiesof the proteins corresponding to genes in the yeastgenome. Our goal is to study the effectiveness of approachesthat utilize all data sources that are availablein this problem setting, including unlabeled and relationaldata, and abstracts of research papers. We study transductionand co-training for using unlabeled data. We investigatea propositionalization approach which uses relationalgene interaction data. We study the benefit of informationextraction for utilizing a collection of scientific abstracts.The studied tasks are KDD Cup tasks of 2001 and 2002.The solutions which we describe achieved the highest scorefor task 2 in 2001, the fourth rank for task 3 in 2001, thehighest score for one of the two subtasks and the third placefor the overall task 2 in 2002.