T. Joachims

Publications231
h-index72
Citations56,987
Highly Influential Citations6,164
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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…
Optimizing search engines using clickthrough data
TLDR
The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking.
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 and give guidelines for the application of SVMs to large domains.
Large Margin Methods for Structured and Interdependent Output Variables
TLDR
This paper proposes to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation and presents a cutting plane algorithm that solves the optimization problem in polynomial time for a large class of problems.
Transductive Inference for Text Classification using Support Vector Machines
TLDR
An analysis of why Transductive Support Vector Machines are well suited for text classi cation is presented, and an algorithm for training TSVMs, handling 10,000 examples and more is proposed.
Support vector machine learning for interdependent and structured output spaces
TLDR
This paper proposes to generalize multiclass Support Vector Machine learning in a formulation that involves features extracted jointly from inputs and outputs, and demonstrates the versatility and effectiveness of the method on problems ranging from supervised grammar learning and named-entity recognition, to taxonomic text classification and sequence alignment.
Training linear SVMs in linear time
TLDR
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 and several orders of magnitude faster than decomposition methods like svm light for large datasets.
Making large-scale support vector machine learning practical
TLDR
This chapter presents algorithmic and computational results developed for SV M light V2.0, which make large-scale SVM training more practical and give guidelines for the application of SVMs to large domains.
Cutting-plane training of structural SVMs
TLDR
This paper explores how cutting-plane methods can provide fast training not only for classification SVMs, but also for structural SVMs and presents an extensive empirical evaluation of the method applied to binary classification, multi-class classification, HMM sequence tagging, and CFG parsing.
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
TLDR
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 and suggests that the probabilistic algorithms are preferable to the heuristic Rocchio classifier.
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