T. Joachims

Publications225
h-index67
Citations52,886
Highly Influential Citations5,664
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Text Categorization with Support Vector Machines: Learning with Many Relevant Features
TLDR
This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. Expand
  • 8,186
  • 655
  • PDF
Optimizing search engines using clickthrough data
TLDR
This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data by analyzing which links the users click on in the presented ranking. Expand
  • 4,089
  • 599
  • PDF
Making large scale SVM learning practical
TLDR
This chapter presents algorithmic and computational results developed for SVM light V 2.0, which make large-scale SVM training more practical. Expand
  • 5,364
  • 506
  • PDF
Large Margin Methods for Structured and Interdependent Output Variables
TLDR
This paper addresses the general problem of learning a mapping from inputvectors or patterns x ∈ X to discrete response variablesy ∈ Y . Expand
  • 2,185
  • 430
  • PDF
Transductive Inference for Text Classification using Support Vector Machines
TLDR
This paper introduces Transductive Support Vector Machines (TSVMs) for text classi cation. Expand
  • 2,912
  • 398
  • PDF
Support vector machine learning for interdependent and structured output spaces
TLDR
We propose to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs. Expand
  • 1,420
  • 304
  • PDF
Training linear SVMs in linear time
  • T. Joachims
  • Mathematics, Computer Science
  • KDD '06
  • 20 August 2006
TLDR
This paper presents a Cutting Plane Algorithm for training linear SVMs that provably has training time 0(s,n) for classification problems and o(sn log (n)) for ordinal regression problems. Expand
  • 2,002
  • 293
  • PDF
Making large-scale support vector machine learning practical
TLDR
This chapter presents an improved algorithm for training SVMs on large-scale problems and describes its eecient implementation. Expand
  • 1,811
  • 174
Cutting-plane training of structural SVMs
TLDR
We show that for an equivalent “1-slack” reformulation of the linear SVM training problem, our cutting-plane method has time complexity linear in the number of training examples. Expand
  • 1,067
  • 169
  • PDF
Learning to classify text using support vector machines - methods, theory and algorithms
  • T. Joachims
  • Computer Science
  • The Kluwer international series in engineering…
  • 1 April 2002
TLDR
Learning to Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples that combines high performance and efficiency with theoretical understanding and improved robustness. Expand
  • 1,752
  • 140