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185
Estimation of probabilities from sparse data for the language model component of a speech recognizer
 IEEE Transactions on Acoustics, Speech and Signal Processing
, 1987
"... AbstractThe description of a novel type of rngram language model is given. The model offers, via a nonlinear recursive procedure, a computation and space efficient solution to the problem of estimating probabilities from sparse data. This solution compares favorably to other proposed methods. Wh ..."
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Cited by 799 (2 self)
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. While the method has been developed for and successfully implemented in the IBM Real Time Speech Recognizers, its generality makes it applicable in other areas where the problem of estimating probabilities from sparse data arises. Sparseness of data is an inherent property of any real text
Selforganized language modeling for speech recognition
 Readings in Speech Recognition
, 1990
"... In the case of a trlgr~m language model, the probability of the next word conditioned on the previous two words is estimated from a large corpus of text. The resulting static trigram language model (STLM) has fixed probabilities that are independent of the document being dictated. To improve the l ..."
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Cited by 394 (6 self)
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in this paper a simple model based on the trigram frequencies estimated from the partially dictated document. We call this model ~ cache trigram language model (CTLM) since we are c~chlng the recent history of words. We have found that the CTLM red,aces the perplexity of a dictated document by 23%. The error
Multi view registration for novelty/background separation
 Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conf. on,pp.757,764
, 2012
"... We propose a system for the automatic segmentation of novelties from the background in scenarios where multiple images of the same environment are available e.g. obtained by wearable visual cameras. Our method finds the pixels in a query image corresponding to the underlying background environment ..."
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Cited by 2 (0 self)
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pixel, in the query image, belonging to the background by computing its appearance inconsistency to the multiple reference images. We then, produce multiple segmentations of the query image using an iterated graph cuts algorithm, initializing from these estimated probabilities and consecutively combine
Nonparametric Maximum Likelihood Estimation of Probability Measures: Existence
, 2004
"... This paper formulates the nonparametric maximum likelihood estimation of probability measures and generalizes the consistency result on the maximum likelihood estimator (MLE). We drop the independence assumption on the underlying stochastic process and replace it with the assumption that the stoch ..."
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This paper formulates the nonparametric maximum likelihood estimation of probability measures and generalizes the consistency result on the maximum likelihood estimator (MLE). We drop the independence assumption on the underlying stochastic process and replace it with the assumption
Imputation of Missing Values when the Probability of Response Depends on the Variable Being Imputed
 Journal of the American Statistical Association
, 1982
"... A method is developed for imputing missing values when the cases in which it is not ignorable. Section 3 presents the probability of response depends upon the variable the derivation of the maximum likelihood estimator of being imputed. The missing data problem is viewed as the parameters of our mod ..."
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Cited by 31 (0 self)
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A method is developed for imputing missing values when the cases in which it is not ignorable. Section 3 presents the probability of response depends upon the variable the derivation of the maximum likelihood estimator of being imputed. The missing data problem is viewed as the parameters of our
ON THE USE OF MLPDISTANCE TO ESTIMATE POSTERIOR PROBABILITIES BY KNN FOR SPEECH RECOGNITION
"... In this work we try to estimate a posteriori probabilities needed for speech recognition by the KNearestNeighbors rule (KNN), using a MultiLayered Perceptrom (MLP) to obtain the distances between neighbors. Thus, we can distinguish two different works: on the one hand, we estimate a posteriori p ..."
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In this work we try to estimate a posteriori probabilities needed for speech recognition by the KNearestNeighbors rule (KNN), using a MultiLayered Perceptrom (MLP) to obtain the distances between neighbors. Thus, we can distinguish two different works: on the one hand, we estimate a posteriori
Optimizing Local Probability Models for StatisticalParsing
"... Abstract. This paper studies the properties and performance of models for estimating local probability distributions which are used as components of larger probabilistic systems historybased generative parsing models. We report experimental results showing that memorybased learning outperforms ..."
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Abstract. This paper studies the properties and performance of models for estimating local probability distributions which are used as components of larger probabilistic systems historybased generative parsing models. We report experimental results showing that memorybased learning
A Fuzzy ARTMAP Nonparametric Probability Estimator for Nonstationary Pattern Recognition Problems
 IEEE Transactions on Neural Networks
, 1995
"... Abstract An incremental, nonparametric probability estimation procedure using the fuzzy ARTMAP (adaptive resonance theorysupehed predictive mapping) neural network is htrodud. In the slowlearning mode, fuzzy ARTMAP searches for patterns of data on which to build ever more accurate estimates. In m ..."
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Cited by 25 (2 self)
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. In maxnodes mode, the network initially learns a 6xed number of categories, and weights are then adjusted gradually. I. FUZZY ARTMAP FOR PROBABILITY ESTIMATION ANY pattern recognition applications require an esM timate of the probability that an input belongs to a given class. In a medical database
Probability Concepts For An Expert System Used For Data Fusion
"... Probability concepts for rulebaaed expert systems are developed that are compatible with probability used in data fusion of imprecise information Procedures for treating probabilistic evidence are presented, which include the effects of statistical dependence. Confidence limits are defined as bein ..."
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as being proportional to rootmeansquare errors in estimates, and a method is outlined that allows the confidence limits in the probability estimate of the hypothesis to be expressed in terms of the confidence limits in the estimate of the evidence. Procedures are outlined for weighting and combining
Calculation of LTC Premiums based on direct estimates of transition probabilities
"... In this paper we model the lifehistory of LTC patients using a Markovian multistate model in order to calculate premiums for a given LTCplan. Instead of estimating the transition intensities in this model we use the approach suggested by Andersen et al. (2003) for a direct estimation of the tran ..."
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values and the covariates of our data are given by a GLM with the logit as linkfunction. Since the GLMs do not allow for correlation between successive observations we use instead the ”Generalized Estimating Equations ” (GEEs) to estimate the parameters of our regression model. The approach is illustrated using a
Results 1  10
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185