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Field Support Vector Machines

Publication ,  Journal Article
Huang, K; Jiang, H; Zhang, XY
Published in: IEEE Transactions on Emerging Topics in Computational Intelligence
December 1, 2017

Conventional classifiers often regard input samples as identically and independently distributed (i.i.d.). This is however not true in many real applications, especially when patterns occur as groups (where each group shares a homogeneous style). Such tasks are also called field classification. By breaking the i.i.d. assumption, one novel framework called Field Support Vector Machine (F-SVM) is proposed in this paper. The distinction lies that it is capable of training and predicting a group of patterns (i.e., a field pattern) simultaneously. Specifically, the proposed F-SVM classifier is investigated by learning simultaneously both the classifier and the Style Normalization Transformation for each group of data (called field). Such joint learning proves even feasible in the high-dimensional kernel space. An efficient optimization algorithm is further developed with the convergence guaranteed. More importantly, by appropriately exploring the style consistency in each field, the F-SVM is able to significantly improve the classification accuracy. A series of experiments are conducted to verify the effectiveness of the F-SVM model. Empirical results show that the proposed F-SVM achieves in three different benchmark data sets the best performance so far, significantly better than those state-of-the-art classifiers.

Duke Scholars

Published In

IEEE Transactions on Emerging Topics in Computational Intelligence

DOI

EISSN

2471-285X

Publication Date

December 1, 2017

Volume

1

Issue

6

Start / End Page

454 / 463

Related Subject Headings

  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
 

Citation

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Huang, K., Jiang, H., & Zhang, X. Y. (2017). Field Support Vector Machines. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(6), 454–463. https://doi.org/10.1109/TETCI.2017.2751062
Huang, K., H. Jiang, and X. Y. Zhang. “Field Support Vector Machines.” IEEE Transactions on Emerging Topics in Computational Intelligence 1, no. 6 (December 1, 2017): 454–63. https://doi.org/10.1109/TETCI.2017.2751062.
Huang K, Jiang H, Zhang XY. Field Support Vector Machines. IEEE Transactions on Emerging Topics in Computational Intelligence. 2017 Dec 1;1(6):454–63.
Huang, K., et al. “Field Support Vector Machines.” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 1, no. 6, Dec. 2017, pp. 454–63. Scopus, doi:10.1109/TETCI.2017.2751062.
Huang K, Jiang H, Zhang XY. Field Support Vector Machines. IEEE Transactions on Emerging Topics in Computational Intelligence. 2017 Dec 1;1(6):454–463.

Published In

IEEE Transactions on Emerging Topics in Computational Intelligence

DOI

EISSN

2471-285X

Publication Date

December 1, 2017

Volume

1

Issue

6

Start / End Page

454 / 463

Related Subject Headings

  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation