Learning in neural spatial interaction models: A statistical perspective

Publication: Scientific journalJournal articlepeer-review


In this paper we view learning as an unconstrained non-linear minimization
problem in which the objective function is defined by the negative log-likelihood
function and the search space by the parameter space of an origin constrained product
unit neural spatial interaction model. We consider Alopex based global search, as
opposed to local search based upon backpropagation of gradient descents, each in
combination with the bootstrapping pairs approach to solve the maximum likelihood
learning problem. Interregional telecommunication traffic flow data from Austria are
used as test bed for comparing the performance of the two learning procedures. The
study illustrates the superiority of Alopex based global search, measured in terms of
Kullback and Leibler's information criterion.
Original languageEnglish
Pages (from-to)287 - 299
JournalJournal of Geographical Systems
Issue number3
Publication statusPublished - 2002

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