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- 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)

- 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)

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)

- 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
- 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)

- Thorsten Joachims
- ICML
- 1999

- 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)

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)

- Thorsten Joachims, Laura A. Granka, Bing Pan, Helene Hembrooke, Geri Gay
- SIGIR
- 2005

This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments… (More)

- Matthew Schultz, Thorsten Joachims
- NIPS
- 2003

This paper presents a method for learning a distance metric from relative comparison such as " A is closer to B than A is to C ". Taking a Support Vector Machine (SVM) approach, we develop an algorithm that provides a flexible way of describing qualitative training data as a set of constraints. We show that such constraints lead to a convex quadratic… (More)