Text Categorization with Support Vector Machines: Learning with Many Relevant Features
- T. Joachims
- Computer ScienceEuropean Conference on Machine Learning
- 21 April 1998
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
- T. Joachims
- Computer ScienceKnowledge Discovery and Data Mining
- 23 July 2002
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
- T. Joachims
- Computer Science
- 1998
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.
Transductive Inference for Text Classification using Support Vector Machines
- T. Joachims
- Computer ScienceInternational Conference on Machine Learning
- 27 June 1999
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.
Large Margin Methods for Structured and Interdependent Output Variables
- Ioannis Tsochantaridis, T. Joachims, Thomas Hofmann, Y. Altun
- Computer ScienceJournal of machine learning research
- 1 December 2005
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.
Training linear SVMs in linear time
- T. Joachims
- Computer ScienceKnowledge Discovery and Data Mining
- 20 August 2006
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.
Support vector machine learning for interdependent and structured output spaces
- Ioannis Tsochantaridis, Thomas Hofmann, T. Joachims, Y. Altun
- Computer ScienceInternational Conference on Machine Learning
- 4 July 2004
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.
Making large-scale support vector machine learning practical
- T. Joachims
- Computer Science
- 8 February 1999
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
- T. Joachims, Thomas Finley, C. Yu
- Computer ScienceMachine-mediated learning
- 1 October 2009
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.
Learning structural SVMs with latent variables
- C. Yu, T. Joachims
- Computer ScienceInternational Conference on Machine Learning
- 14 June 2009
A large-margin formulation and algorithm for structured output prediction that allows the use of latent variables and the generality and performance of the approach is demonstrated through three applications including motiffinding, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.
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