T. Joachims

Publications234
h-index71
Citations52,006
Highly Influential Citations5,688
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Text Categorization with Support Vector Machines: Learning with Many Relevant Features
This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are… Expand
  • 7,697
  • 619
  • Open Access
Optimizing search engines using clickthrough data
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… Expand
  • 4,021
  • 605
  • Open Access
Making large scale SVM learning practical
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… Expand
  • 5,324
  • 523
  • Open Access
Large Margin Methods for Structured and Interdependent Output Variables
Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly… Expand
  • 2,159
  • 430
  • Open Access
Transductive Inference for Text Classification using Support Vector Machines
This paper introduces Transductive Support Vector Machines (TSVMs) for text classi cation. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task,… Expand
  • 2,870
  • 396
  • Open Access
Support vector machine learning for interdependent and structured output spaces
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.… Expand
  • 1,411
  • 305
  • Open Access
Training linear SVMs in linear time
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,… Expand
  • 1,952
  • 288
  • Open Access
Making large-scale support vector machine learning practical
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… Expand
  • 1,791
  • 177
Cutting-plane training of structural SVMs
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… Expand
  • 1,061
  • 167
  • Open Access
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
Abstract : A probabilistic analysis of the Rocchio relevance feedback algorithm, one of the most popular learning methods from information retrieval, is presented in a text categorization framework.… Expand
  • 1,440
  • 142
  • Open Access