ISSN:
0885-6125
Keywords:
ID3
;
backpropagation
;
experimental comparisons
;
text-to-speech
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be closely matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially in this domain.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1022822623726
