Cascade Support Vector Machines with Dimensionality Reduction
Author
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-01-15
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets.
In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing.
The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level.
The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level.
We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping.
The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.
American Psychological Association (APA)
Kramer, Oliver. 2015. Cascade Support Vector Machines with Dimensionality Reduction. Applied Computational Intelligence and Soft Computing،Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1052213
Modern Language Association (MLA)
Kramer, Oliver. Cascade Support Vector Machines with Dimensionality Reduction. Applied Computational Intelligence and Soft Computing No. 2015 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1052213
American Medical Association (AMA)
Kramer, Oliver. Cascade Support Vector Machines with Dimensionality Reduction. Applied Computational Intelligence and Soft Computing. 2015. Vol. 2015, no. 2015, pp.1-8.
https://search.emarefa.net/detail/BIM-1052213
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1052213