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Abstract: We propose a framework to incorporate unlabeled data in kernel classifier, based on the idea that two points in the same cluster are more likely to have the same label. This is achieved by modifying the eigenspectrum of the kernel matrix. Experimental results assess the validity of this approach. (Update)
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BibTeX entry: (Update)
O. Chapelle, J. Weston, and B. Sch olkopf. Cluster kernels for semi-supervised learning. In NIPS, volume 15, 2003. http://citeseer.ist.psu.edu/chapelle03cluster.html More
@incollection{ chapelle03cluster,
author = "O. Chapelle and J. Weston and B. Sch{\"o}lkopf",
title = "Cluster kernels for semi-supervised learning",
series = "NIPS",
volume = "15",
year = "2003",
url = "citeseer.ist.psu.edu/chapelle03cluster.html" }
Citations (may not include all citations):180 Combining labeled and unlabeled data with co-training - Blum, Mitchell - 1998
119 Exploiting generative models in discriminative classi- ers - Jaakkola, Haussler - 1998
80 Learning to classify text from labeled and unlabeled documen.. - Nigam, McCallum et al. - 1998
79 Nonlinear component analysis as a kernel eigenvalue problem - Sch, Smola et al. - 1998
72 On spectral clustering: Analysis and an algorithm - Ng, Jordan et al. - 2001
57 Segmentation using eigenvectors: A unifying view - Weiss - 1999
38 Choosing multiple parameters for support vector machines - Chapelle, Vapnik et al. - 2002
33 Learning with labeled and unlabeled data - Seeger - 2001
11 Transductive inference for text classi cation using support .. (context) - Joachims - 1999
9 Covariance kernels from Bayesian generative models - Seeger - 2001
8 Marginalized kernels for biological sequences (context) - Tsuda, Kin et al. - 2002
6 Partially labeled classi cation with markov random walks (context) - Szummer, Jaakkola - 2001
4 Generalization bounds via eigenvalues of the Gram matrix - Sch, Shawe-Taylor et al. - 1999
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