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\iteman{io-port 05112967}
\itemau{Ney, Hermann; Essen, Ute; Kneser, Reinhard}
\itemti{On the Estimation of 'Small' Probabilities by Leaving-One-Out.}
\itemso{IEEE Transactions on Pattern Analysis and Machine Intelligence 17, No.12, 1202-1212 (1995).}
\itemab
Summary: Abstract --- In this paper, 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.
\itemrv{~}
\itemcc{}
\itemut{generalization capability.}
\itemli{doi:10.1109/34.476512}
\end