Abstract
This paper proposes a multi-neural network classification based on fisher transformation. The new method improves HDR [1] (Hierarchical discriminate regression) method proposed in 2000, which can classify the training set from coarse to fine by non-linear dynamic clustering for high-dimension data. In proposed method a fisher subspace replaces K-L subspace of HDR that simplifies the Hierarchical tree. Simulation results show that our method is better than HDR on recognition ratio and time cost.
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References
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© 2005 Springer-Verlag Berlin Heidelberg
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Chen, D., Lu, X., Zhang, L. (2005). Fisher Subspace Tree Classifier Based on Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_3
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DOI: https://doi.org/10.1007/11427445_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)