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https://www.researchgate.net/publication/41781683_Transductive_Support_Vector_Machines_for_Structured_Variables
We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible ...
https://www.cs.uni-potsdam.de/ml/publications/icml2007-transductive.pdf
Transductive Support Vector Machines for Structured Variables 3. Unconstrained Optimization for Structured Output Spaces Optimization Problem 1 is the known SVM learning problem in input output spaces with cost-based mar-gin rescaling,which includes the xedsize marginwith 0/1-loss as special case. All presented results can also
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.8225
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding ...
https://en.wikipedia.org/wiki/Support_vector_machines
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.8596
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding ...
https://pure.mpg.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_1790474
Zien, A., Brefeld, U., & Scheffer, T. (2007). Transductive Support Vector Machines for Structured Variables.Talk presented at International Conference on Machine ...Cited by: 57
https://www.cs.cornell.edu/people/tj/svm_light/index.html
For multivariate and structured outputs use SVM struct. ... SVM light is an implementation of Vapnik's Support Vector Machine [Vapnik, 1995] for the problem of pattern recognition, for the problem of regression, ... Training algorithm for transductive Support Vector Machines.
https://debategraph.org/details.aspx?nid=292481
Transductive support vector machines . Transductive support vector machines extend SVMs in that they could also treat partially labeled data in semi-supervised learning by following the principles of transduction. Here, in addition to the training set , the learner …
http://www.cs.cmu.edu/~guestrin/Class/10701-S06/Slides/tsvms-pca.pdf
Transductive support vector machines (TSVMs) w. x + b = + 1 w. x ... variables! [Vapnik 98] w. x ... transductive SVMs What is transductive v. semi-supervised learning Formulation for transductive SVM can also be used for semi-supervised learning Optimization is hard! Integer program
https://core.ac.uk/display/45870064
Transductive learning can be reduced to combinatorial optimization problems over all possible labelings of the unlabeled data. In order to scale transductive learning to structured variables, we transform the corresponding non-convex, combinatorial, constrained optimization problems into continuous, unconstrained optimization problems.Author: A. Zien, U. Brefeld and T. Scheffer
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