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Transductive Inference for Text Classification using Support VectorMachines
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Transductive Inference for Text Classification using Support VectorMachines   (Citations: 859)
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This paper introduces transductive support vector machines (TSVMs) for text classification. While regular support vector machines (SVMs) try to induce a general decision function for a learning task, TSVMs take into account a particular test set and try to minimize misclassifications of just those particular examples. The paper presents an analysis of why TSVMs are well suited for text classification. These theoretical findings are supported by experiments on three test collections. The experiments show substantial improvements over inductive methods, especially for small training sets, cutting the number of labeled training examples down to a 20th on some tasks. This work also proposes an algorithm for training TSVMs efficiently, handling 10,000 examples and more.
Conference: International Conference on Machine Learning - ICML , pp. 200-209, 1999
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    • ...Since the works of Joachims [15], [16], researchers have successfully applied SVM to many related tasks and presented some convincing results [5], [6], [27], [39], [55]...

    Ning Zhong, et al. Effective Pattern Discovery for Text Mining

    • ...However, a number of solutions have been proposed with varying degrees of success, including an integer programming method [23], a combinatorial approach [24], and a sequential optimization procedure [25]...
    • ...Minimizing (24) over θ and z is very hard, so one could include prior knowledge about the label z, as is done in many semisupervised SVM algorithms, by using a ratio constraint between positive and negative data, also called a balancing constraint [11], [24], [42]...
    • ...1) S 3 VM − light [24], which uses a combinatorial search...
    • ...SEMISUPERVISED LS-SVM, S3 VM − Light [24], S3 VM − CCCP [49] AND S 3 VM − GA [48] LEARN THE SEMISUPERVISED SVM, WHILE THE...

    Mathias M. Adankon, et al. Semisupervised Learning Using Bayesian Interpretation: Application to ...

    • ...Methods for learning with unlabeled examples include using a biased classifier [24], one-class SVMs [32], [33], and transductive SVMs [21]...

    Aditya Kumar Sehgal, et al. Identifying Relevant Data for a Biological Database: Handcrafted Rules...

    • ...Generally, semi-supervised learning methods fall into four major categories, i.e., generative-modelbased methods [26-28] , low density separation based methods [29-31] , graph-based methods [32-34] , and disagreement-based methods [35-39] ...
    • ...Note that an alternative way to address the problems of \lack of su‐cient labeled data" and \data imbalance" simultaneously by imposing a \class proportion" constraint over a special type of base learner, which can adjust the portion of labeling of unlabeled data according to the constraint, just as what TSVM [31] does...

    Yuan Jiang, et al. Software Defect Detection with Rocus

    • ...One approach presented in [6] starts with an initial SVM solution obtained from the labeled data alone...

    Karim All, et al. FlowBoost — Appearance learning from sparsely annotated video

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