article · IEEE Transactions on Neural Networks · 2003

Classification in a normalized feature space using support vector machines

Cited by 113Abstract and details only

Abstract

This paper discusses classification using support vector machines in a normalized feature space. We consider both normalization in input space and in feature space. Exploiting the fact that in this setting all points lie on the surface of a unit hypersphere we replace the optimal separating hyperplane by one that is symmetric in its angles, leading to an improved estimator. Evaluation of these considerations is done in numerical experiments on two real-world datasets. The stability to noise of this offset correction is subsequently investigated as well as its optimality.

Cite this paper

APA 7

Graf, A., Smola, A., & Borer, S. (2003). Classification in a normalized feature space using support vector machines. IEEE Transactions on Neural Networks, 14(3), 597–605. https://doi.org/10.1109/tnn.2003.811708

MLA 9

Graf, Andrea, et al. “Classification in a normalized feature space using support vector machines.” IEEE Transactions on Neural Networks, vol. 14, no. 3, 2003, pp. 597–605, https://doi.org/10.1109/tnn.2003.811708.

Chicago

Graf, Andrea, Alex Smola, and Silvio Borer. 2003. “Classification in a normalized feature space using support vector machines.” IEEE Transactions on Neural Networks 14 (3): 597–605. https://doi.org/10.1109/tnn.2003.811708.

Harvard

Graf, A., Smola, A. and Borer, S. (2003) ‘Classification in a normalized feature space using support vector machines’, IEEE Transactions on Neural Networks, 14(3), pp. 597–605. Available at: https://doi.org/10.1109/tnn.2003.811708.

BibTeX

@article{graf2003classification,
  title = {{Classification in a normalized feature space using support vector machines}},
  author = {Graf, Andrea and Smola, Alex and Borer, Silvio},
  journal = {IEEE Transactions on Neural Networks},
  volume = {14},
  number = {3},
  pages = {597--605},
  year = {2003},
  doi = {10.1109/tnn.2003.811708}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Classification in a normalized feature space using support vector machines

Abstract

Cite this paper

The discrete analytical hyperspheres

A Determination Method for Separation Hyperplane

A new maximal-margin spherical-structured multi-class support vector machine

Separation hyperplane based on support vector domain description

Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere