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- W2162826313 abstract "We present new approaches for semi-supervised learning based on the formulations of SVMs for the conventional supervised setting. The manifold structure of the data points given by the graph Laplacian can be taken into account in a efficient way. The proposed optimization problems fully enjoy the sparse structure of the graph Laplacian, which enables us to optimize the problems with a large number of data points in a practical amount of computational time. Some results of experiments showing the performance of our approaches are presented." @default.
- W2162826313 created "2016-06-24" @default.
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- W2162826313 date "2006-03-22" @default.
- W2162826313 modified "2023-09-26" @default.
- W2162826313 title "Optimization Approaches for Semi-Supervised Learning" @default.
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- W2162826313 doi "https://doi.org/10.1109/icmla.2005.50" @default.
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