Keywords
Scale (ratio)Support vector machineComputer scienceArtificial intelligenceMachine learningGeographyCartography
Related Publications
Trust Region Newton Method for Logistic Regression
Large-scale logistic regression arises in many applications such as document classification and natural language processing. In this paper, we apply a trust region Newton method...
Advances in kernel methods: support vector learning
Introduction to support vector learning roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik generalization performance of ...
LIBLINEAR: A Library for Large Linear Classification
LIBLINEAR is an open source library for large-scale linear classification. It supports logistic regression and linear support vector machines. We provide easy-to-use command-lin...
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 pro...
Large-Scale Kernel Machines
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistical...
Publication Info
- Year
- 1998
- Type
- book-chapter
- Citations
- 1560
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
1560
OpenAlex
Cite This
Thorsten Joachims
(1998).
Making Large-Scale Support Vector Machine Learning Practical.
The MIT Press eBooks
.
https://doi.org/10.7551/mitpress/1130.003.0015
Identifiers
- DOI
- 10.7551/mitpress/1130.003.0015