Abstract
We propose a structural perceptron for supervised and unsupervised learning on data represented in terms of attributed graphs. We integrate structural perceptrons into a multi-layer perceptron and competitive learning network to provide examples of supervised and unsupervised neural learning machines which are suited to process graphs. In first experiments the proposed algorithms were successfully applied to function regression, classification, and clustering.
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Jain, B.J., Wysotzki, F. (2004). Structural Perceptrons for Attributed Graphs. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_8
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DOI: https://doi.org/10.1007/978-3-540-27868-9_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22570-6
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