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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)

- Thorsten Joachims
- KDD
- 2002

This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they… (More)

- Thorsten Joachims
- ECML
- 1998

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)

- Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun
- Journal of Machine Learning Research
- 2005

(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)

- Thorsten Joachims
- ICML
- 1999

- Thorsten Joachims
- KDD
- 2006

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)

- Chun-Nam John Yu, Thorsten Joachims
- ICML
- 2009

We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application problems, with an optimization problem that can be solved efficiently using Concave-Convex Programming. The generality and performance of the approach is demonstrated through three… (More)

- Thorsten Joachims, Thomas Finley, Chun-Nam John Yu
- Machine Learning
- 2009

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)

- Thorsten Joachims
- KI
- 2005

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)