Bickel, S., & Scheffer, T. (2004). Multi-view clustering. In Proceedings of the IEEE international conference on data mining (pp. 19â26). Blaschko, M., & Lampert, C. (2008). Correlational spectral clustering. Computer Vision and Pattern Recognition. DOI:10.1109/CVPR.2008.4587353. CVPR 2008. IEEE Conference on pp. 1â8 (2008). Google Scholar Blaschko, M. B., Lampert, C. H., & Gretton, A. (2008). Sem
Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution. Often, however, we have plentiful labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and little or no labeled training data. In this work we investigate two questions. First, und
GraphLab: A Parallel Framework for Machine LearningDesigning and implementing efficient and provably correct parallel machine learning (ML) algorithms can be very challenging. Existing high-level parallel abstractions like MapReduce are often insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common pat
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