Abstract
In this paper, we propose a new network-growing algorithm which is called the cost-sensitive greedy network-growing algorithm. This new method can maximize information while controlling the associated cost. Experimantal results show that the cost minimization approximates input patterns as much as possible, while information maximization aims to extract distinctive features.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kamimura, R., Uchida, O. (2004). Cost-Sensitive Greedy Network-Growing Algorithm with Gaussian Activation Functions. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_100
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DOI: https://doi.org/10.1007/978-3-540-30499-9_100
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
Online ISBN: 978-3-540-30499-9
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