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Building Support Vector Machines with Reduced Classifier Complexity

2006

Article

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Support vector machines (SVMs), though accurate, are not preferred in applications requiring great classification speed, due to the number of support vectors being large. To overcome this problem we devise a primal method with the following properties: (1) it decouples the idea of basis functions from the concept of support vectors; (2) it greedily finds a set of kernel basis functions of a specified maximum size ($dmax$) to approximate the SVM primal cost function well; (3) it is efficient and roughly scales as $O(ndmax^2)$ where $n$ is the number of training examples; and, (4) the number of basis functions it requires to achieve an accuracy close to the SVM accuracy is usually far less than the number of SVM support vectors.

Author(s): Keerthi, S. and Chapelle, O. and DeCoste, D.
Journal: Journal of Machine Learning Research
Volume: 7
Pages: 1493-1515
Year: 2006
Month: July
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@article{3598,
  title = {Building Support Vector Machines with Reduced Classifier Complexity},
  author = {Keerthi, S. and Chapelle, O. and DeCoste, D.},
  journal = {Journal of Machine Learning Research},
  volume = {7},
  pages = {1493-1515},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2006},
  month_numeric = {7}
}