preprint available

[Joachim Utans]

The following preprint has been placed in the neuroprose archive
at Ohio State University:



       Selecting Neural Network Architectures via the Prediction Risk:
                Application to Corporate Bond Rating Prediction

     Joachim Utans                          John Moody
     Department of Electrical Engineering   Department of Computer Science
     Yale University                        Yale University
     New Haven, CT 06520                    New Haven, CT 06520



                               Abstract:

    Intuitively, the notion of generalization is closely related to the
    ability of an estimator to perform well with new observations. In
    this paper, we propose the prediction risk as a measure of the
    generalization ability of multi-layer perceptron networks and use it
    to select the optimal network architecture. The prediction risk needs
    to be estimated from the available data; here we approximate the
    prediction risk by v-fold cross-validation and asymtotic estimates of
    generalized cross-validation or Akaike's final prediction error. We
    apply the technique to the problem of predicting corporate bond
    ratings. This problem is very attractive as a case study, since it is
    characterized by the limited availability of the data and by the lack
    of complete a priori information that could be used to impose a
    structure to the network architecture.



To retrieve it by anonymous ftp:

unix> ftp cheops.cis.ohio-state.edu          # (or ftp 128.146.8.62)
Name (cheops.cis.ohio-state.edu:): anonymous
Password (cheops.cis.ohio-state.edu:anonymous): neuron
ftp> cd pub/neuroprose
ftp> binary
ftp> get utans.bondrating.ps.Z
ftp> quit
unix> uncompress utans.bondrating.ps
unix> lpr -P(your_local_postscript_printer) utans.bondrating.ps


	Joachim Utans