Matches in SemOpenAlex for { <https://semopenalex.org/work/W2169884321> ?p ?o ?g. }
- W2169884321 endingPage "1592" @default.
- W2169884321 startingPage "1585" @default.
- W2169884321 abstract "Semi-supervised learning algorithms have been successfully applied in many applications with scarce labeled data, by utilizing the unlabeled data. One important category is graph based semi-supervised learning algorithms, for which the performance depends considerably on the quality of the graph, or its hyperparameters. In this paper, we deal with the less explored problem of learning the graphs. We propose a graph learning method for the harmonic energy minimization method; this is done by minimizing the leave-one-out prediction error on labeled data points. We use a gradient based method and designed an efficient algorithm which significantly accelerates the calculation of the gradient by applying the matrix inversion lemma and using careful pre-computation. Experimental results show that the graph learning method is effective in improving the performance of the classification algorithm." @default.
- W2169884321 created "2016-06-24" @default.
- W2169884321 creator A5038911681 @default.
- W2169884321 creator A5080105995 @default.
- W2169884321 date "2007-09-07" @default.
- W2169884321 modified "2023-10-17" @default.
- W2169884321 title "Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms" @default.
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