C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning - Special issue on inductive transfer
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Theoretical models of learning to learn
Learning to learn
Data integration using similarity joins and a word-based information representation language
ACM Transactions on Information Systems (TOIS)
A theoretical framework for learning from a pool of disparate data sources
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A framework for simultaneous co-clustering and learning from complex data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic framework for relational clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning from Multiple Sources
The Journal of Machine Learning Research
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
CLAP: Collaborative pattern mining for distributed information systems
Decision Support Systems
Quality of information-based source assessment and selection
Neurocomputing
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Many applications are facing the problem of learning from an objective dataset, whereas information from other auxiliary sources may be beneficial but cannot be integrated into the objective dataset for learning. In this paper, we propose an omni-view learning approach to enable learning from multiple data collections. The theme is to organize heterogeneous data sources into a unified table with global data view. To achieve the omni-view learning goal, we consider that the objective dataset and the auxiliary datasets share some instance-level dependency structures. We then propose a relational k-means to cluster instances in each auxiliary dataset, such that clusters can help build new features to capture correlations between the objective and auxiliary datasets. Experimental results demonstrate that omni-view learning can help build models which outperform the ones learned from the objective dataset only. Comparisons with the co-training algorithm further assert that omni-view learning provides an alternative, yet effective, way for semi-supervised learning.