Comments on 'On a novel unsupervised competitive learning algorithm for scalar quantization'

Author(s)

Andrew, Lachlan L. H.

Available versions

Abstract

A recent letter presented a novel neural-network learning rule, BAR, (boundary adaptation rule) which was shown to converge to a scalar quantizer with equiprobable outputs. Such quantizers will be called maximum entropy quantizers (MEQs). It is interesting that such a simple rule can produce these quantizers. Its practical usefulness is limited, however, by two factors. First, there are more efficient algorithms which yield better results, and second MEQs are unsuitable for many quantization tasks, as discussed below.

Publication year

1996

Publication type

Journal article

Source

IEEE Transactions on Neural Networks, Vol. 7, no. 1 (Jan 1996), pp. 254-256

ISSN

1045-9227

Publisher

IEEE

Copyright

Copyright © 1996 IEEE. Paper is reproduced in accordance with the copyright policy of the publisher.

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