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Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
 ADVANCES IN LARGE MARGIN CLASSIFIERS
, 1999
"... The output of a classifier should be a calibrated posterior probability to enable postprocessing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score. Howev ..."
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Cited by 1051 (0 self)
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The output of a classifier should be a calibrated posterior probability to enable postprocessing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score
Speaker verification using Adapted Gaussian mixture models
 Digital Signal Processing
, 2000
"... In this paper we describe the major elements of MIT Lincoln Laboratory’s Gaussian mixture model (GMM)based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs). The system is built around the likelihood ratio test for verification, using simple but ef ..."
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Cited by 1010 (42 self)
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but effective GMMs for likelihood functions, a universal background model (UBM) for alternative speaker representation, and a form of Bayesian adaptation to derive speaker models from the UBM. The development and use of a handset detector and score normalization to greatly improve verification performance
Longitudinal data analysis using generalized linear models”.
 Biometrika,
, 1986
"... SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence. The estimating ..."
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Cited by 1526 (8 self)
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. The estimating equations are derived without specifying the joint distribution of a subject's observations yet they reduce to the score equations for multivariate Gaussian outcomes. Asymptotic theory is presented for the general class of estimators. Specific cases in which we assume independence, m
A Program for Aligning Sentences in Bilingual Corpora
, 1993
"... This paper will describe a method and a program (align) for aligning sentences based on a simple statistical model of character lengths. The program uses the fact that longer sentences in one language tend to be translated into longer sentences in the other language, and that shorter sentences tend ..."
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Cited by 529 (5 self)
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to be translated into shorter sentences. A probabilistic score is assigned to each proposed correspondence of sentences, based on the scaled difference of lengths of the two sentences (in characters) and the variance of this difference. This probabilistic score is used in a dynamic programming framework to find
Discrete Choice Methods with Simulation
, 2002
"... This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logi ..."
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Cited by 1326 (20 self)
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: logit, generalized extreme value (including nested and crossnested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulationassisted estimation procedures are investigated and compared, including maximum simulated likelihood, the method of simulated
Proceedings of the Survey Methods Section AN APPROXIMATION OF THE PARTIAL LIKELIHOOD SCORE IN A JOINT DESIGNMODEL SPACE
"... An approximation to the Sample Partial Likelihood Score (SPLS) in design probability was first proposed by Binder (1992) and then considered by Lin (2000), in order to derive asymptotic theory for the parameters of the proportional hazards regression model. However they did not provide conditions un ..."
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An approximation to the Sample Partial Likelihood Score (SPLS) in design probability was first proposed by Binder (1992) and then considered by Lin (2000), in order to derive asymptotic theory for the parameters of the proportional hazards regression model. However they did not provide conditions
Eurospeech 2001 Scandinavia Estimating Pronunciation Variations from Acoustic Likelihood Score for HMM Reconstruction
"... It is widely acknowledged that pronunciation modeling is an efficient way to improve recognition performance in spontaneous speech. In pronunciation modeling, almost all methods of generating variation probability are based on relative frequency counting from DP alignment. In this paper, we investig ..."
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investigate the local model mismatching caused by pronunciation variations and propose to estimate variation probability from acoustic likelihood score. According to estimated probability, we present a method of reconstructing pretrained HMM models to include alternate pronunciations by sharing optimal
Discriminative Reranking for Natural Language Parsing
, 2005
"... This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this i ..."
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Cited by 333 (9 self)
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takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). We apply the boosting method to parsing the Wall Street Journal treebank. The method combined the loglikelihood under a baseline
The Bayesian Structural EM Algorithm
, 1998
"... In recent years there has been a flurry of works on learning Bayesian networks from data. One of the hard problems in this area is how to effectively learn the structure of a belief network from incomplete datathat is, in the presence of missing values or hidden variables. In a recent paper, I in ..."
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Cited by 260 (13 self)
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introduced an algorithm called Structural EM that combines the standard Expectation Maximization (EM) algorithm, which optimizes parameters, with structure search for model selection. That algorithm learns networks based on penalized likelihood scores, which include the BIC/MDL score and various
Maximum score estimation of the stochastic utility model of choice
 Journal of Econometrics
, 1975
"... This paper introduces a class of robust estimators of the parameters of a stochastic utility function. Existing maximum likelihood and regression estimation methods require the assumption of a particular distributional family for the random component of utility. In contrast, estimators of the ‘maxi ..."
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Cited by 204 (2 self)
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This paper introduces a class of robust estimators of the parameters of a stochastic utility function. Existing maximum likelihood and regression estimation methods require the assumption of a particular distributional family for the random component of utility. In contrast, estimators
Results 1  10
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