Search Machine Learning Repository: @inproceedings{icml2014c2_fang14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically},
    Url = {http://jmlr.org/proceedings/papers/v32/fang14.pdf},
    Abstract = {As the central notion in semi-supervised learning, smoothness is often realized on a graph representation of the data. In this paper, we study two complementary dimensions of smoothness: its pointwise nature and probabilistic modeling. While no existing graph-based work exploits them in conjunction, we encompass both in a novel framework of Probabilistic Graph-based Pointwise Smoothness (PGP), building upon two foundational models of data closeness and label coupling. This new form of smoothness axiomatizes a set of probability constraints, which ultimately enables class prediction. Theoretically, we provide an error and robustness analysis of PGP. Empirically, we conduct extensive experiments to show the advantages of PGP.},
    Author = {Yuan Fang and Kevin Chang and Hady Lauw},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {406-414}
   }