Transductive Support Vector Machines For Structured Variables

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Transductive Support Vector Machines for Structured Variables

    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 ...

Transductive Support Vector Machines for Structured Variables

    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

Transductive support vector machines for structured ...

    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 ...

Support-vector machine - Wikipedia

    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 …

Transductive support vector machines for structured ...

    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 ...

Transductive Support Vector Machines for Structured ...

    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

SVM-Light: Support Vector Machine - Cornell University

    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.

Details view: 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 …

Transductive SVMs

    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

Transductive Support Vector Machines for Structured Variables

    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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