Paper available on neural network ensembles

[Anders Krogh]

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The paper is 8 pages long.

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       Neural Network Ensembles, Cross Validation, and Active Learning

                    by Anders Krogh and Jesper Vedelsby



Abstract:
Learning of continuous valued functions using neural network ensembles
(committees) can give improved accuracy, reliable estimation of the
generalization error, and active learning.  The ambiguity is defined as
the variation of the output of ensemble members averaged over unlabeled
data, so it quantifies the disagreement among the networks.  It is
discussed how to use the ambiguity in combination with cross-validation
to give a reliable estimate of the ensemble generalization error, and
how this type of ensemble cross-validation can sometimes improve
performance.  It is shown how to estimate the optimal weights of the
ensemble members using unlabeled data.  By a generalization of query by
committee, it is finally shown how the ambiguity can be used to select
new training data to be labeled in an active learning scheme.


The paper will appear in
G. Tesauro, D. S. Touretzky and T. K. Leen, eds.,
"Advances in Neural Information Processing Systems 7",
MIT Press, Cambridge MA, 1995.


________________________________________
 
Anders Krogh

Nordita
Blegdamsvej 17, 2100 Copenhagen, Denmark

email: krogh at nordita.dk
Phone: +45 3532 5503
Fax:   +45 3138 9157
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