Abstract
Many classifiers and methods are proposed to deal with letter recognition problem. Among them, clustering is a widely used method. But only one time for clustering is not adequately. Here, we adopt data preprocessing and a re kernel clustering method to tackle the letter recognition problem. In order to validate effectiveness and efficiency of proposed method, we introduce re kernel clustering into Kernel Nearest Neighbor classification (KNN), Radial Basis Function Neural Network (RBFNN), and Support Vector Machine (SVM). Furthermore, we compare the difference between re kernel clustering and one time kernel clustering which is denoted as kernel clustering for short. Experimental results validate that re kernel clustering forms fewer and more feasible kernels and attain higher classification accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
D. Q. Gao and J. Li. Kernel fisher discriminants and kernel nearest neighbor classifiers: a comparative study for large-scale learning problems. International Joint Conference on Neural Networks, 2006, China, 1333–1338.
S. Wu, X. D. Feng, and W. J. Zhou. Spectral clustering of high-dimensional data exploiting sparse representation vectors. Neurocomputing, 135(2014), 229–239.
X. B. Zhi, J. L. Fan, and F. Zhao. Robust local feature weighting hard c-means clustering algorithm. Neurocomputing, 134(2014) 20–29.
I. Triguero, J. A. Sáez, J. Luengo, S. García, and F. Herrera. On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification. Neurocomputing, 132(2014), 30–41.
S. K Oh, W. D Kim, W. Pedrycz, and S. C Joo. Design of K-means clustering-based polynomial radial basis function neural networks (pRBF NNs) realized with the aid of particle swarm optimization and differential evolution. Neurocomputing, 78(2012)1, 121–132.
Z. N. Wu, H. G. Zhang, and J. H. Liu. A fuzzy support vector machine algorithm for classification based on a novel PIM fuzzy clustering method. Neurocomputing, 125(2014), 119–124.
I. C. Yeh, X. Y. Zhang, C. Wu, and K. C. Huang. Radial basis function networks with adjustable kernel shape parameters. 2010 International Conference on Machine Learning and Cybernetics (ICMLC), Qingdao, China, 2010, Vol. 3, 1482–1485.
D. Q. Gao and T. Zhang. Support vector machine classifiers using RBF kernels with clustering-based centers and widths. Proceedings of International Joint Conference on Neural Networks. 2007.
S. Haikin. Neural Networks: A Comprehensive Foundation. 1998.
K. P. FRS. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(1901)11, 559–572.
I. T. Jolliffe and MyiLibrary. Principal component analysis, Wiley Online Library, Vol. 2, 2002.
C. Saunders, J. Shawe-Taylor, and A. Vinokourov. String kernels, fisher kernels and finite state automata. Advances in Neural Information Processing Systems 15, 2003.
Available in ‘http://archive.ics.uci.edu/ml/’, 2006.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Science Foundation (No. IIS-9988642) and the Multidisciplinary Research Program.
About this article
Cite this article
Zhu, C., Gao, D. Data preprocessing and re kernel clustering for letter. J. Electron.(China) 31, 552–564 (2014). https://doi.org/10.1007/s11767-014-4113-7
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11767-014-4113-7