Abstract
A new nonparametric feature mapping technique for pattern classification is proposed and compared experimentally with a principal component and Sammon's mapping methods. We use the mapped training-.set vectors for an active weights initialization of the multilauer perception classier in a (wo-variate mapped .space. Simulations have shown a usefulness of the proposed weights initialization method for designing the pereeptrons when we need to obtain highly nonlinear decision boundaries.
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© 1998 Springer-Verlag Berlin Heidelberg
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Raudys, A. (1998). A nonparametric data mapping technique for active initialization of the multilayer perceptron. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033329
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DOI: https://doi.org/10.1007/BFb0033329
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