| Seeger, M.: Learning with labeled and unlabeled data. Technical report, University of Edinburgh (2002) |
....[6, 31] are also applied to the problem, but finding kernels for some input spaces can be a tedious job. A complete reviewing of the methods used for the semi supervised task is out of the scope of this paper. For a better and more extensive description of such strategies, see for example [26, 27] We are inspired from the area of the Co training methods [4, 17, 26] Co training methods suppose that structural knowledge of the data is available and aims at learning to use a restricted view on the examples, by trying for example to find features that are coherent between different input ....
Matthias Seeger. Learning with labeled and unlabeled data. Technical report, Inst. for Adaptive and Neural Computation, Univ. of Edinburgh, 2001.
....non of the features as descendants. As we are interested in using unlabeled data in learning the Bayesian network classifier, we restrict ourselves to generative classifiers and exclude structures that are diagnostic, which cannot be trained using maximum likelihood approaches with unlabeled data [23, 21]. Two examples of generative Bayesian network classifiers are the Naive Bayes (NB) classifier, in which the features are assumed independent given the class, and the TreeAugmented Naive Bayes classifier (TAN) The NB classifier makes the assumption that all features are conditionally independent ....
M. Seeger. Learning with labeled and unlabeled data. Technical report, Edinburgh University, 2001.
....learning, Boltzmann machine 1 Introduction In many classi cation applications, labeled training data are scarce but unlabeled data are abundant. It is very useful if we can use unlabeled data to aid labeled data in learning a classi er. Semi supervised learning deals with exactly this problem [See01]. We assume the dataset in question has the property that nearby (under some local distance metric, e.g. Euclidean) data points tend to have the same labels. Under this assumption the spatial distribution of data, revealed by large amount of unlabeled data, is correlated to classi cation. We can ....
Matthias Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2001.
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Seeger, M.: Learning with labeled and unlabeled data. Technical report, University of Edinburgh (2002)
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M. Seeger. Learning with labeled and unlabeled data. Inst. for Adaptive and Neural Computation, technical report, Univ. of Edinburgh, 2001.
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M. Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2002.
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Seeger, M. (2001). Learning with labeled and unlabeled data (Technical Report). University of Edinburgh.
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M. Seeger. Learning with labeled and unlabeled data. In Technical Report, University of Edinburgh, 2001.
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Seeger, M. (2001). Learning with labeled and unlabeled data (Technical Report). University of Edinburgh.
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M. Seeger. Learning with labeled and unlabeled data. Technical report, Institute for Adaptive and Neural Computation, University of Edinburgh, 2001.
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M. Seeger. Learning with labeled and unlabeled data. Technical Report, Institute for Adaptive and Neural Computation, University of Edinburgh, 2001.
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M. Seeger. Learning with labeled and unlabeled data. Technical report, Edinburgh University, 2001.
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Matthias Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2001.
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M. Seeger, "Learning with labeled and unlabeled data," Edinburgh University, United Kingdom, tech. rep., 2001.
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Seeger, M. (2000b). Learning with labeled and unlabeled data. Technical report, Institute for ANC, Edinburgh, UK. See http://www.dai.ed.ac.uk/seeger/papers.html.
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M. Seeger. Learning with labeled and unlabeled data, 2000.
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Seeger, M. (2000b). Learning with labeled and unlabeled data. Technical report, Institute for ANC, Edinburgh, UK. See http://www.dai.ed.ac.uk/seeger/papers.html.
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M. Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2001.
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M. Seeger. Learning with labeled and unlabeled data. In Technical Report. Edinburgh University, UK, 2001.
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M. Seeger. Learning with labeled and unlabeled data. Technical report, Institute for Adaptive and Neural Computation, University of Edinburgh, 2001. http://www.dai.ed.ac.uk/homes/seeger/papers/review.ps.gz.
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Seeger, M., "Learning with labeled and unlabeled data," Technical report, University of Edinburgh, 2000.
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M. Seeger. Learning with labeled and unlabeled data. Technical report, Edinburgh University, 2001.
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M. Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2001.
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M. Seeger. Learning with labeled and unlabeled data. Technical report, The University of Edinburgh, 2000.
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Matthias Seeger. Learning with labeled and unlabeled data. Unpublished. http://www.dai.ed.ac.uk/homes/seeger/, February 2001.
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