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Seeger, M.: Learning with labeled and unlabeled data. Technical report, University of Edinburgh (2002)

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A Mixed Ensemble Approach for the Semi-Supervised Problem - Dimitriadou, Weingessel..   (Correct)

....[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.


Learning Bayesian Network Classifiers for Facial.. - Cohen, Sebe.. (2003)   (2 citations)  (Correct)

....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.


Towards Semi-Supervised Classification with Markov Random Fields - Zhu, Ghahramani (2002)   (Correct)

....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.


Integrating Utility into Face De-Identification - Ralph Gross Edoardo   (Correct)

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Seeger, M.: Learning with labeled and unlabeled data. Technical report, University of Edinburgh (2002)


Manifold Regularization: A Geometric Framework for Learning .. - Belkin, Niyogi, al. (2006)   (Correct)

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M. Seeger. Learning with labeled and unlabeled data. Inst. for Adaptive and Neural Computation, technical report, Univ. of Edinburgh, 2001.


Generalization in Clustering with Unobserved Features - Krupka, Tishby (2005)   (Correct)

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M. Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2002.


Combining Active Learning and Semi-Supervised Learning - Using Gaussian Fields   (Correct)

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Seeger, M. (2001). Learning with labeled and unlabeled data (Technical Report). University of Edinburgh.


Privacy Leakage in Multi-relational Databases via Pattern - Based Semi-Supervised..   (Correct)

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M. Seeger. Learning with labeled and unlabeled data. In Technical Report, University of Edinburgh, 2001.


Combining Active Learning and Semi-Supervised Learning - Using Gaussian Fields   (Correct)

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Seeger, M. (2001). Learning with labeled and unlabeled data (Technical Report). University of Edinburgh.


A New Variational Framework for Rigid-Body Alignment - Kato, Tsuda, Tomii, Asai (2004)   (Correct)

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M. Seeger. Learning with labeled and unlabeled data. Technical report, Institute for Adaptive and Neural Computation, University of Edinburgh, 2001.


Semi-Supervised Training of Models for Appearance-Based.. - Rosenberg (2004)   (Correct)

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M. Seeger. Learning with labeled and unlabeled data. Technical Report, Institute for Adaptive and Neural Computation, University of Edinburgh, 2001.


Cluster Kernels for Semi-Supervised Learning - Chapelle, Weston, Schölkopf (2003)   (17 citations)  (Correct)

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M. Seeger. Learning with labeled and unlabeled data. Technical report, Edinburgh University, 2001.


Semi-Supervised Learning: From Gaussian Fields to.. - Zhu, Lafferty, Ghahramani (2003)   (1 citation)  (Correct)

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Matthias Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2001.


Semisupervised Learning Of Classifiers With Application To.. - Cohen (2003)   (1 citation)  (Correct)

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M. Seeger, "Learning with labeled and unlabeled data," Edinburgh University, United Kingdom, tech. rep., 2001.


Learning Translations from Comparable Corpora - Talbot (2003)   (Correct)

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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.


A Note on Semi-Supervised Learning using Markov Random Fields - Li, McCallum (2004)   (Correct)

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M. Seeger. Learning with labeled and unlabeled data, 2000.


Learning Translations from Comparable Corpora - Talbot (2003)   (Correct)

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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.


Improving Object Classification in Far-Field Video - Bose, Grimson (2004)   (3 citations)  (Correct)

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M. Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2001.


The Effect of Unlabeled Data on Generative Classifiers.. - Cohen, Cozman, Bronstein (2002)   (Correct)

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M. Seeger. Learning with labeled and unlabeled data. In Technical Report. Edinburgh University, UK, 2001.


Clustering with the Fisher Score - Tsuda, Kawanabe, Müller   (Correct)

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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.


Learning to Use Scene Context for Object Classification in.. - Biswajit Bose And (2003)   (Correct)

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Seeger, M., "Learning with labeled and unlabeled data," Technical report, University of Edinburgh, 2000.


Semi-Supervised Learning for Facial Expression - Recognition Ira Cohen   (Correct)

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M. Seeger. Learning with labeled and unlabeled data. Technical report, Edinburgh University, 2001.


Semi-Supervised Protein Classification Using Cluster.. - Weston, Leslie, Zhou.. (2003)   (2 citations)  (Correct)

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M. Seeger. Learning with labeled and unlabeled data. Technical report, University of Edinburgh, 2001.


Learning with Local and Global Consistency - Zhou, Bousquet, Lal, Weston.. (2003)   (12 citations)  (Correct)

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M. Seeger. Learning with labeled and unlabeled data. Technical report, The University of Edinburgh, 2000.


Learning from Partially Labeled Data - Szummer (2002)   (Correct)

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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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