Abstract
The previous work matrix-pattern-oriented Ho-Kashyap classifier (MatMHKS) can directly deal with images in matrix representation n 1×n 2 such that the spatial information within these images is not destroyed. Although MatMHKS works with n 1×n 2 per image, it is far less that this spatial correlation with the matrix form n 1×n 2 can suggest the real number of freedom. MatMHKS just keeps the relationship of the pixels in the same row or column of images. In this paper we further consider the relationship of the pixels that close to each other may be correlated, and thus develop a new matrix-pattern-oriented Ho-Kashyap classifier named MatHKLSS that is introduced with a locally spatial smoothness. The experimental results here demonstrate that the proposed MatHKLSS has a superior advantage to MatMHKS in terms of classification.
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Wang, Z., Chen, S., Gao, D. (2009). A Novel Matrix-Pattern-Oriented Ho-Kashyap Classifier with Locally Spatial Smoothness. In: Wang, H., Shen, Y., Huang, T., Zeng, Z. (eds) The Sixth International Symposium on Neural Networks (ISNN 2009). Advances in Intelligent and Soft Computing, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01216-7_46
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DOI: https://doi.org/10.1007/978-3-642-01216-7_46
Publisher Name: Springer, Berlin, Heidelberg
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