{"id":124,"date":"2016-07-07T13:03:07","date_gmt":"2016-07-07T13:03:07","guid":{"rendered":"http:\/\/semisupervised-learning.compute.dtu.dk\/?page_id=124"},"modified":"2016-08-08T10:45:52","modified_gmt":"2016-08-08T10:45:52","slug":"reading-material","status":"publish","type":"page","link":"http:\/\/semisupervised-learning.compute.dtu.dk\/?page_id=124","title":{"rendered":"Reading material"},"content":{"rendered":"<p><em>Update: Exercise material and slides can be found under <a href=\"http:\/\/semisupervised-learning.compute.dtu.dk\/?page_id=31\">Invited Speakers<\/a>, below the description of the relevant speaker.<\/em><br \/>\n<strong><br \/>\nClassics of semi-supervised learning:<\/strong><\/p>\n<ul>\n<li>Castelli, V. and Cover, T.M., 1995. On the exponential value of labeled samples. Pattern Recognition Letters, 16(1), pp.105-111.<\/li>\n<li>Blum, A. and Mitchell, T., 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the eleventh annual conference on Computational learning theory (pp. 92-100). ACM.<\/li>\n<li>Nigam, K., McCallum, A.K., Thrun, S. and Mitchell, T., 2000. Text classification from labeled and unlabeled documents using EM. Machine learning, 39(2-3), pp.103-134.<\/li>\n<li>L.J.P. van der Maaten and K.Q. Weinberger. Stochastic Triplet Embedding. To appear in\u00a0Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012.<\/li>\n<li>Tamuz, Omer; Liu, Ce; Belongie, Serge; Shamir, Ohad; Kalai, Adam.\u00a0Adaptively Learning the Crowd Kernel.\u00a0International Conference on Machine Learning (ICML),\u00a0Bellevue, WA,\u00a02011.<\/li>\n<li><span style=\"line-height: 1.5;\">Chopra, Sumit, Raia Hadsell, and Yann LeCun. &#8220;Learning a similarity metric discriminatively, with application to face verification.&#8221;\u00a0CVPR 2005.\u00a0<\/span><\/li>\n<li>Cohen, I., Cozman, F. G., Sebe, N., Cirelo, M. C., &amp; Huang, T. S. (2004). Semisupervised learning of classifiers: Theory, algorithms, and their application to human-computer interaction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(12), 1553-1566.<\/li>\n<li>Joachims, T. (1999, June). Transductive inference for text classification using support vector machines. In ICML (Vol. 99, pp. 200-209).<\/li>\n<\/ul>\n<h5>EM, self-learning, and assumptions in SSL<\/h5>\n<ul>\n<li>Loog, M. (2016). Contrastive pessimistic likelihood estimation for semi-supervised classification. IEEE transactions on pattern analysis and machine intelligence, 38(3), 462-475.<\/li>\n<li>Krijthe, J. H., &amp; Loog, M. (2016). Projected Estimators for Robust Semi-supervised Classification. arXiv preprint arXiv:1602.07865.<\/li>\n<li>Loog, M., Krijthe, J. H., &amp; Jensen, A. C., On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL, in: C. H. Chen (Ed.), Handbook of Pattern Recognition and Computer Vision, 5th Edition, World Scientific, 2016, Ch. 1.3.<\/li>\n<\/ul>\n<h5><strong>Learning perceptual embeddings with triplet based comparisons:<\/strong><\/h5>\n<ul>\n<li>Wilber, Michael; Kwak, Sam; Belongie, Serge. Cost-Effective HITs for Relative Similarity Comparisons, Human Computation and Crowdsourcing (HCOMP), Pittsburgh, 2014.<\/li>\n<li>Wilber, Michael; Kwak, Iljung; Kriegman, David; Belongie, Serge. Learning Concept Embeddings with Combined Human-Machine Expertise, International Conference on Computer Vision (ICCV), 2015.<\/li>\n<\/ul>\n<h5><strong>Variational auto-encoders and their application to semi-supervised learning:<\/strong><\/h5>\n<ul>\n<li>Kingma, Diederik P and Mohamed, Shakir and Rezende, Danilo Jimenez and Welling, Max. Semi-supervised Learning with Deep Generative Models, Advances in Neural Information Processing Systems, p. 3581-3589, 2014.<\/li>\n<li>Kingma, Diederik P and Welling, Max. Auto-Encoding Variational Bayes, arXiv preprint arXiv:1312.6114, 2013.<\/li>\n<li>Rezende, Danilo Jimenez and Mohamed, Shakir and Wierstra, Daan. Stochastic Backpropagation and Approximate Inference in Deep Generative Models, arXiv preprint arXiv:1401.4082, 2014.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h5><strong>Bonus material: <\/strong><\/h5>\n<ul>\n<li>Seeger, M., 2000. Learning with labeled and unlabeled data (No. EPFL-REPORT-161327)<\/li>\n<li>Chapelle, O., Sch\u00f6lkopf, B. and Zien, A., 2006. Semi-Supervised Learning.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Update: Exercise material and slides can be found under Invited Speakers, below the description of the relevant speaker. Classics of semi-supervised learning: Castelli, V. and Cover, T.M., 1995. On the exponential value of labeled samples. Pattern Recognition Letters, 16(1), pp.105-111. Blum, A. and Mitchell, T., 1998. Combining labeled and unlabeled data with co-training. In Proceedings &hellip; <a href=\"http:\/\/semisupervised-learning.compute.dtu.dk\/?page_id=124\" class=\"more-link\">Continue reading <span class=\"screen-reader-text\">Reading material<\/span> <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-124","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/semisupervised-learning.compute.dtu.dk\/index.php?rest_route=\/wp\/v2\/pages\/124","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/semisupervised-learning.compute.dtu.dk\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/semisupervised-learning.compute.dtu.dk\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/semisupervised-learning.compute.dtu.dk\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/semisupervised-learning.compute.dtu.dk\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=124"}],"version-history":[{"count":6,"href":"http:\/\/semisupervised-learning.compute.dtu.dk\/index.php?rest_route=\/wp\/v2\/pages\/124\/revisions"}],"predecessor-version":[{"id":266,"href":"http:\/\/semisupervised-learning.compute.dtu.dk\/index.php?rest_route=\/wp\/v2\/pages\/124\/revisions\/266"}],"wp:attachment":[{"href":"http:\/\/semisupervised-learning.compute.dtu.dk\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=124"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}