Normalization in Support Vector Machines

@inproceedings{Graf2001NormalizationIS,
  title={Normalization in Support Vector Machines},
  author={Arnulf B. A. Graf and S. Borer},
  booktitle={DAGM-Symposium},
  year={2001}
}
  • Arnulf B. A. Graf, S. Borer
  • Published in DAGM-Symposium 2001
  • Computer Science, Mathematics
  • This article deals with various aspects of normalization in the context of Support Vector Machines. We consider fist normalization of the vectors in the input space and point out the inherent limitations. A natural extension to the feature space is then represented by the kernel function normalization. A correction of the position of the Optimal Separating Hyperplane is subsequently introduced so as to suit better these normalized kernels. Numerical experiments finally evaluate the different… CONTINUE READING
    86 Citations

    Figures and Topics from this paper

    Explore Further: Topics Discussed in This Paper

    Classification in a normalized feature space using support vector machines
    • 115
    • PDF
    Improved Support Vector Machine Generalization Using Normalized Input Space
    • 31
    Support vector machines: A distance-based approach to multi-class classification
    • Wissam Aoudi, A. Barbar
    • Mathematics
    • 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET)
    • 2016
    • 2
    A generalized-space expansion of Support Vector Machines for diagnostic systems
    • I. Dimou, M. Zervakis
    • Mathematics
    • Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine
    • 2010
    Properties of the sample estimators used for statistical normalization of feature vectors
    Simple Techniques Make Sense: Feature Pooling and Normalization for Image Classification
    • 19
    • Highly Influenced
    • PDF

    References

    SHOWING 1-8 OF 8 REFERENCES
    Support Vector Machines for 3D Object Recognition
    • M. Pontil, A. Verri
    • Mathematics, Computer Science
    • IEEE Trans. Pattern Anal. Mach. Intell.
    • 1998
    • 851
    • PDF
    Support vector learning
    • 621
    Estimating the Support of a High-Dimensional Distribution
    • 4,183
    • PDF
    A PAC-Bayesian margin bound for linear classifiers
    • 97
    • PDF
    On the Computational Power of Winner-Take-All
    • W. Maass
    • Computer Science, Medicine
    • Neural Computation
    • 2000
    • 300
    • PDF
    Advances in Kernel Methods
    • Advances in Kernel Methods
    • 1999
    Neural Networks: a Comprehensive Approach
    • Neural Networks: a Comprehensive Approach
    • 1999
    Support Vector Learning. R. Oldenburg Verlag
    • Support Vector Learning. R. Oldenburg Verlag
    • 1997