Learning nonrecursive definitions of relations with LINUS
EWSL-91 Proceedings of the European working session on learning on Machine learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Learning to construct knowledge bases from the World Wide Web
Artificial Intelligence - Special issue on Intelligent internet systems
Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
ACM SIGKDD Explorations Newsletter
Relational Data Mining
Propositionalization approaches to relational data mining
Relational Data Mining
The Frame-Based Module of the SUISEKI Information Extraction System
IEEE Intelligent Systems
Adaptive View Validation: A First Step Towards Automatic View Detection
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning word normalization using word suffix and context from unlabeled data
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Efficient Theta-Subsumption Based on Graph Algorithms
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
The genomics of a signaling pathway: a KDD Cup challenge task
ACM SIGKDD Explorations Newsletter
One class SVM for yeast regulation prediction
ACM SIGKDD Explorations Newsletter
Predicting the effects of gene deletion
ACM SIGKDD Explorations Newsletter
Combining data and text mining techniques for yeast gene regulation prediction: a case study
ACM SIGKDD Explorations Newsletter
Feature engineering for a gene regulation prediction task
ACM SIGKDD Explorations Newsletter
P-tree classification of yeast gene deletion data
ACM SIGKDD Explorations Newsletter
Editorial: Data Mining Lessons Learned
Machine Learning
Open Set Face Recognition Using Transduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning from Skewed Class Multi-relational Databases
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
A relational approach to probabilistic classification in a transductive setting
Engineering Applications of Artificial Intelligence
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
The Journal of Machine Learning Research
Exploiting propositionalization based on random relational rules for semi-supervised learning
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Artificial Intelligence in Medicine
Transductive relational classification in the co-training paradigm
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Learning from Skewed Class Multi-relational Databases
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Semi-supervised learning using greedy max-cut
The Journal of Machine Learning Research
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We focus on the problem of predicting functional properties of the proteins corresponding to genes in the yeast genome. Our goal is to study the effectiveness of approaches that utilize all data sources that are available in this problem setting, including relational data, abstracts of research papers, and unlabeled data. We investigate a propositionalization approach which uses relational gene interaction data. We study the benefit of text classification and information extraction for utilizing a collection of scientific abstracts. We study transduction and co-training for using unlabeled data. We report on both, positive and negative results on the investigated approaches. The studied tasks are KDD Cup tasks of 2001 and 2002. The solutions which we describe achieved the highest score for task 2 in 2001, the fourth rank for task 3 in 2001, the highest score for one of the two subtasks and the third place for the overall task 2 in 2002.