Multiple Information Sources Cooperative Learning.

Conference Paper · January 2009with6 Reads
Source: DBLP
Conference: IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009
Many applications are facing the problem of learn- ing 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 multi- ple data collections. The theme is to organize het- erogeneous data sources into a unified table with global data view. To achieve the omni-view learn- ing goal, we consider that the objective dataset and the auxiliary datasets share some instance-level dependency structures. We then propose a rela- tional k-means to cluster instances in each auxil- iary dataset, such that clusters can help build new features to capture correlations between the objec- tive 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.
    • "Learning from T 4 is more challenging than learning from T 1 , T 2 , and T 3 because examples in T 4 are unlabeled and have different distributions from the target domain. In this section, we introduce a relational k-means (RK) [17] based learning model which aims to construct some new features to the labeled examples by using information extracted from unlabeled instances in T 4 . An example of the RK learning is shown inFig. "
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