article

Exploring margin setting for good generalization in multiple class discrimination

Authors:

H. John Caulfield,

Kaveh HeidaryAuthors Info & Claims

Pattern Recognition, Volume 38, Issue 8

Pages 1225 - 1238

https://doi.org/10.1016/j.patcog.2005.01.009

Published: 01 August 2005 Publication History

Abstract

In earlier publications, we showed that it is possible to achieve both low VC dimension and high accuracy, if we divide the given training set into a sequence of subsets each of which does admit such a solution. Here we explore in substantially more detail how the various steps in what was called ''Margin Setting'' impact false classification and indecision rates. A complex relationship exists between margin size, the number of steps in the process, and those two classification failures. After mapping those relationships, we offer a qualitative explanation of them.

References

[1]

Burges, C.J.C., A Tutorial on Support Vector Machines for Pattern Recognition. 1998. Kluwer Academic Publishers, Dordrecht.

Google Scholar

[2]

Valiant, L.G., A theory of learnability. Commun. ACM. v27. 1134-1142.

Digital Library

Google Scholar

[3]

Schapire, R.E., The strength of weak learnability. Mach. Learn. v5 i2. 197-227.

Crossref

Google Scholar

[4]

Caulfield, H.J., Independent task Fourier filters. Opt. Eng. v40. 2414-2418.

Crossref

Google Scholar

[5]

Caulfield, H.J., Karavolos, A. and Ludman, J.E., Improving optical Fourier pattern recognition by accommodating the missing information. Inf. Sci. v162 i1. 35-52.

Crossref

Google Scholar

[6]

H.J. Caulfield, Generalization in heteroassociative neural networks, Neural Comput. (2003), submitted for publication.

Google Scholar

[7]

R.A. Fisher, The use of multiple measurements in taxonomic problems, Ann. Eugen. (1936) 179-188.

Google Scholar

[8]

Domeniconi, C., Peng, J. and Gunopulos, D., Locally adaptive metric nearest-neighbor classification. IEEE Trans. Pattern Recogn. Mach. Intell. v24 i9. 1281-1285.

Crossref

Google Scholar

Cited By

View all

Cabrera MWachs J(2018)Biomechanical-Based Approach to Data Augmentation for One-Shot Gesture Recognition2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)10.1109/FG.2018.00016(38-44)Online publication date: 15-May-2018
https://dl.acm.org/doi/10.1109/FG.2018.00016
Fu JCaulfield HYoo SAtluri V(2005)Use of Artificial Color filtering to improve iris recognition and searchingPattern Recognition Letters10.1016/j.patrec.2005.03.03226:14(2244-2251)Online publication date: 15-Oct-2005
https://dl.acm.org/doi/10.1016/j.patrec.2005.03.032

Exploring margin setting for good generalization in multiple class discrimination
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

Recommendations

Posterior probability support vector Machines for unbalanced data

This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and optimal hyperplane. Within this framework, a new ...
A rough margin based support vector machine

By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to outliers. Similar to the classical SVM, the RMSVM searches for the separating hyper-plane ...
Margin calibration in SVM class-imbalanced learning

Imbalanced dataset learning is an important practical issue in machine learning, even in support vector machines (SVMs). In this study, a well known reference model for solving the problem proposed by Veropoulos et al., is first studied. From the aspect ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Pattern Recognition Volume 38, Issue 8

August, 2005

181 pages

ISSN:0031-3203

Issue’s Table of Contents

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 August 2005

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Cabrera MWachs J(2018)Biomechanical-Based Approach to Data Augmentation for One-Shot Gesture Recognition2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)10.1109/FG.2018.00016(38-44)Online publication date: 15-May-2018
https://dl.acm.org/doi/10.1109/FG.2018.00016
Fu JCaulfield HYoo SAtluri V(2005)Use of Artificial Color filtering to improve iris recognition and searchingPattern Recognition Letters10.1016/j.patrec.2005.03.03226:14(2244-2251)Online publication date: 15-Oct-2005
https://dl.acm.org/doi/10.1016/j.patrec.2005.03.032

Abstract

References

Cited By

Recommendations

Posterior probability support vector Machines for unbalanced data

A rough margin based support vector machine

Margin calibration in SVM class-imbalanced learning

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations