Referenced Items are not available for this document.
We apply the leaving-one-out concept to the estimation of `small' probabilities, i.e., the case where the number of training samples is much smaller than the number of possible classes. After deriving the Turing-Good formula in this framework, we introduce several specific models in order to avoid the problems of the original Turing-Good formula. These models are the constrained model, the absolute discounting model and the linear discounting model. These models are then applied to the problem of bigram-based stochastic language modeling. Experimental results are presented for a German and an English corpus
Published in:
Pattern Analysis and Machine Intelligence, IEEE Transactions on
(Volume:17
,
Issue:
12
)
Date of Publication: Dec 1995
- Page(s):
- 1202 - 1212
- ISSN :
- 0162-8828
- INSPEC Accession Number:
- 5148603
- Digital Object Identifier :
- 10.1109/34.476512
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Dec 1995
- Sponsored by :
- IEEE Computer Society
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