Patent
Method and apparatus for transductive support vector machines
TLDR
In this paper, a transductive support vector machine is trained based on labeled training data and unlabeled test data and a non-convex objective function which optimizes a hyperplane classifier for classifying the unlabelled test data is decomposed into a convex function and a concave function.Abstract:
Disclosed is a method for training a transductive support vector machine The support vector machine is trained based on labeled training data and unlabeled test data A non-convex objective function which optimizes a hyperplane classifier for classifying the unlabeled test data is decomposed into a convex function and a concave function A local approximation of the concave function at a hyperplane is calculated, and the approximation of the concave function is combined with the convex function such that the result is a convex problem The convex problem is then solved to determine an updated hyperplane This method is performed iteratively until the solution convergesread more
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Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Book
Fast training of support vector machines using sequential minimal optimization
TL;DR: In this article, the authors proposed a new algorithm for training Support Vector Machines (SVM) called SMO (Sequential Minimal Optimization), which breaks this large QP problem into a series of smallest possible QP problems.
Proceedings Article
Transductive Inference for Text Classification using Support Vector Machines
TL;DR: An analysis of why Transductive Support Vector Machines are well suited for text classi cation is presented, and an algorithm for training TSVMs, handling 10,000 examples and more is proposed.
Proceedings Article
Semi-Supervised Classification by Low Density Separation
Olivier Chapelle,Alexander Zien +1 more
TL;DR: Three semi-supervised algorithms are proposed: deriving graph-based distances that emphazise low density regions between clusters, followed by training a standard SVM, and optimizing the Transductive SVM objective function by gradient descent.
Proceedings Article
Semi-Supervised Support Vector Machines
Kristin P. Bennett,Ayhan Demiriz +1 more
TL;DR: A general S3VM model is proposed that minimizes both the misclassification error and the function capacity based on all the available data that can be converted to a mixed-integer program and then solved exactly using integer programming.
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