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Training a support vector machine SVM leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples, oo-the-shelf(More)
This paper explores the use of Support Vector Machines (SVMs) for learning text classiiers from examples. It analyzes the particular properties of learning with text data and identiies why SVMs are appropriate for this task. Empirical results support the theoretical ndings. SVMs achieve substantial improvements over the currently best performing methods and(More)
(1985). A learning algorithm for boltzmann machines. (2010). Learning the structure of deep sparse graphical models. In AI/Statistics. On tight approximate inference of the logistic-normal topic admixture model. In AI/Statistics.ference using message propoga-tion and topology transformation in vector Gaussian continuous networks. In UAI. Bayesian analysis(More)
Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for high-dimensional sparse data commonly encountered in applications like text classification, word-sense disambiguation, and drug design. These applications involve a large number of examples <i>n</i> as well as a large number of features <i>N</i>,(More)
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input representations. This paper addresses the complementary issue of problems involving complex outputs such as multiple dependent output variables and structured output spaces. We(More)
Discriminative training approaches like structural SVMs have shown much promise for building highly complex and accurate models in areas like natural language processing, protein structure prediction, and information retrieval. However, current training algorithms are computationally expensive or intractable on large datasets. To overcome this bottleneck,(More)
This paper presents a Support Vector Method for optimizing multivariate nonlinear performance measures like the <i>F</i><inf>1</inf>-score. Taking a multivariate prediction approach, we give an algorithm with which such multivariate SVMs can be trained in polynomial time for large classes of potentially non-linear performance measures, in particular ROCArea(More)