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QSAR/QSPR Studies by Kernel Machines, Recursive Neural Networks and Their Integration

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Neural Nets (WIRN 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2859))

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Abstract

We present preliminary results on a comparison between Recurrent Neural Networks (RecNN) and an SVM using a string kernel on QSPR/QSAR problems. In addition to this comparison, we report on a first attempt to combine RecNN with SVM.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Pisa,  

    Alessio Micheli

  2. Department of Pure and Applied Mathematics, University of Padova,  

    Filippo Portera & Alessandro Sperduti

Authors
  1. Alessio Micheli
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  2. Filippo Portera
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  3. Alessandro Sperduti
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Editor information

Editors and Affiliations

  1. Dipartimentimento di Scienze dell’Informazione, via Comelico 39/41, 20135, Milano, Italy

    Bruno Apolloni

  2. Dipartimento di Fisica “E.R. Caianiello”, Università degli Studi di Salerno, Via S. Allende, 84081, Baronissi, (SA), Italy

    Maria Marinaro

  3. Department of Mathematics and Informatics, University of Salerno, Via Ponte Don Melillo, 84084, Fisciano, (SA), Italy

    Roberto Tagliaferri

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© 2003 Springer-Verlag Berlin Heidelberg

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Micheli, A., Portera, F., Sperduti, A. (2003). QSAR/QSPR Studies by Kernel Machines, Recursive Neural Networks and Their Integration. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_35

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  • DOI: https://doi.org/10.1007/978-3-540-45216-4_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20227-1

  • Online ISBN: 978-3-540-45216-4

  • eBook Packages: Springer Book Archive

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