Text categorization with Support Vector Machines: Learning with many relevant features
Lecture notes in computer sciencePublished 1 January 1998
Thorsten Joachims
Citations7,925
SJR quartileQ2
SJR score0.35
SNIP0.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of di-erent learning tasks, eliminating the need for manual parameter tuning.
Abstract
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 appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.
Keywords
Computer Science
The Nature of Statistical Learning Theory
39,279 Citations1995Vladimir Vapnik
Machine LearningSupport-Vector Networks
33,035 Citations1995Corinna Cortes, Vladimir Vapnik
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
C4.5: Programs for Machine Learning
23,665 Citations1992J. R. Quinlan
A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Information Processing & ManagementTerm-weighting approaches in automatic text retrieval
9,532 Citations1988Gerard Salton, Chris Buckley
This paper summarizes the insights gained in automatic term weighting, and provides baseline single term indexing models with which other more elaborate content analysis procedures can be compared.
Lecture notes in computer scienceText categorization with Support Vector Machines: Learning with many relevant features
7,925 Citations1998Thorsten Joachims
SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of di-erent learning tasks, eliminating the need for manual parameter tuning.
Programs for Machine Learning
5,794 Citations1994Steven L. Salzberg, Alberto M. Segre
In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
A Comparative Study on Feature Selection in Text Categorization
4,766 Citations1997Yiming Yang, Jan Pedersen
DF thresholding, the simplest method with the lowest cost in computation, can be reliably used instead of IG or CHI when the computation of these measures are too expensive, and strong correlations between the DF, IG and CHI values of a term are found.
Information RetrievalAn Evaluation of Statistical Approaches to Text Categorization
1,946 Citations1999Yiming Yang
Analysis and empirical evidence suggest that the evaluation results on some versions of Reuters were significantly affected by the inclusion of a large portion of unlabelled documents, mading those results difficult to interpret and leading to considerable confusions in the literature.
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
1,264 Citations1997Thorsten Joachims
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.
Artificial IntelligenceThe perceptron algorithm versus winnow: linear versus logarithmic mistake bounds when few input variables are relevant
87 Citations1997Jyrki Kivinen, Manfred K. Warmuth +1 more
It is shown that an adversary can force any additive algorithm to make (N + k −1) 2 mistakes in learning a monotone disjunction of at most k literals and that for k ⪡ N, Winnow clearly outperforms the Perceptron algorithm also on nonadversarial random data.
The perceptron algorithm vs. Winnow
47 Citations1995Jyrki Kivinen, Manfred K. Warmuth
An adversary strategy is given that forces the Perceptron algorithm to make (N-k+1)/2 mistakes when learning k-literal disjunctions over N variables, which shows that even for simple random data, the number of mistakes made by the PerCEPTron algorithm grows almost linearly with N, even if the number k of relevant variable remains a small constant.