Keywords
Scale (ratio)Support vector machineComputer scienceArtificial intelligenceMachine learningGeographyCartography
Related Publications
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
This chapter describes a new algorithm for training Support Vector Machines: Sequential Minimal Optimization, or SMO. Training a Support Vector Machine (SVM) requires the soluti...
Cross-scale Correlations and the Design and Analysis of Avian Habitat Selection Studies
Abstract It has long been suggested that birds select habitat hierarchically, progressing from coarser to finer spatial scales. This hypothesis, in conjunction with the realizat...
Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors)
Boosting is one of the most important recent developments in\nclassification methodology. Boosting works by sequentially applying a\nclassification algorithm to reweighted versi...
Evolutionary-scale prediction of atomic-level protein structure with a language model
Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full...
Gaussian Processes for Machine Learning
We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over...
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