Abstract
We apply support vector learning to attributed graphs where the kernel matrices are based on approximations of the Schur-Hadamard (SH) inner product by means of recurrent neural networks. We present and discuss experimental results of different classifiers constructed by a SVM operating on positive semi-definite (psd) and non-psd kernel matrices.
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Jain, B.J., Geibel, P., Wysotzki, F. (2004). Combining Recurrent Neural Networks and Support Vector Machines for Structural Pattern Recognition. In: Biundo, S., Frühwirth, T., Palm, G. (eds) KI 2004: Advances in Artificial Intelligence. KI 2004. Lecture Notes in Computer Science(), vol 3238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30221-6_19
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DOI: https://doi.org/10.1007/978-3-540-30221-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23166-0
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