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International Conference on Computational Science and Its Applications

ICCSA 2013: Computational Science and Its Applications – ICCSA 2013 pp 1–16Cite as

Neural Spatial Interaction Models: Network Training, Model Complexity and Generalization Performance

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7974))

Abstract

Spatial interaction models approximate mean interaction frequencies between origin and destination locations by using origin-specific, destination-specific and spatial separation information. The focus is on models that are based on the theory of feedforward neural networks. This contribution considers the functional form of neural spatial interaction models, including the specification of the activation functions, and discusses the problem of network training within a maximum likelihood framework that involves the solution of a non-linear optimization problem. This requires the evaluation of the log-likelihood function with respect to the network parameters. Overfitting is a problem that is likely to occur in neural spatial interaction models. To avoid this problem the contribution recommends controlling the model complexity either by regularization or by early stopping in network training. A bootstrapping pairs approach with replacement may be adopted to evaluate the generalization performance of the models.

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Author information

Authors and Affiliations

  1. Vienna University of Economics and Business, Austria

    Manfred M. Fischer

Authors
  1. Manfred M. Fischer
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Editor information

Editors and Affiliations

  1. L-I.S.U.T. - D.A.P.I.t. Facoltà Ingegneria, Università degli Studi della Basilicata, Viale dell’Ateneo Lucano, 10, 85100, Potenza, Italy

    Beniamino Murgante

  2. Canaanland OTA, Covenant University, Nigeria

    Sanjay Misra

  3. Partimento di Scienze e Tecnologie per LAgricoltura, le Foreste, la Natura e lEnergia, Università degli Studi della Tuscia, Via S. Camillo de Lellis, snc, 01100, Viterbo, Italy

    Maurizio Carlini

  4. Dipartimento di Scienze dell’Ingegneria Civile e dell’Architecttura, Politecnico di Bari, Via Orabona, 4, 70125, Bari, Italy

    Carmelo M. Torre

  5. Quarter 6, Linh Trung, Thu Duc, International University VNU-HCM, Ho Chi Minh City, Vietnam

    Hong-Quang Nguyen

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

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Fischer, M.M. (2013). Neural Spatial Interaction Models: Network Training, Model Complexity and Generalization Performance. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39649-6_1

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  • DOI: https://doi.org/10.1007/978-3-642-39649-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39648-9

  • Online ISBN: 978-3-642-39649-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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