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A Maximum Entropy approach to Natural Language Processing

by Adam L. Berger, Stephen A. Della Pietra , Vincent J. Della Pietra - COMPUTATIONAL LINGUISTICS , 1996
"... The concept of maximum entropy can be traced back along multiple threads to Biblical times. Only recently, however, have computers become powerful enough to permit the widescale application of this concept to real world problems in statistical estimation and pattern recognition. In this paper we des ..."
Abstract - Cited by 1366 (5 self) - Add to MetaCart
describe a method for statistical modeling based on maximum entropy. We present a maximum-likelihood approach for automatically constructing maximum entropy models and describe how to implement this approach efficiently, using as examples several problems in natural language processing.

Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods

by John C. Platt - ADVANCES IN LARGE MARGIN CLASSIFIERS , 1999
"... The output of a classifier should be a calibrated posterior probability to enable post-processing. 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 ..."
Abstract - Cited by 1051 (0 self) - Add to MetaCart
The output of a classifier should be a calibrated posterior probability to enable post-processing. 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

MEGA5: Molecular evolutionary genetics analysis using maximum . . .

by Koichiro Tamura, Daniel Peterson, Nicholas Peterson, Glen Stecher, Masatoshi Nei, Sudhir Kumar , 2011
"... Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version ..."
Abstract - Cited by 7284 (25 self) - Add to MetaCart
5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML

Quartet puzzling: a quartet maximum likelihood method for reconstructing tree topologies.

by Korbinian Strimmer , Arndt Von Haeseler - Mol. Biol. Evol. , 1996
"... A versatile method, quartet puzzling, is introduced to reconstruct the topology (branching pattern) of a phylogenetic tree based on DNA or amino acid sequence data. This method applies maximum-likelihood tree reconstruction to all possible quartets that can be formed from n sequences. The quartet t ..."
Abstract - Cited by 433 (9 self) - Add to MetaCart
A versatile method, quartet puzzling, is introduced to reconstruct the topology (branching pattern) of a phylogenetic tree based on DNA or amino acid sequence data. This method applies maximum-likelihood tree reconstruction to all possible quartets that can be formed from n sequences. The quartet

Statistical Analysis of Cointegrated Vectors

by Soren Johansen - Journal of Economic Dynamics and Control , 1988
"... We consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors. We then derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number of dimen ..."
Abstract - Cited by 2749 (12 self) - Add to MetaCart
We consider a nonstationary vector autoregressive process which is integrated of order 1, and generated by i.i.d. Gaussian errors. We then derive the maximum likelihood estimator of the space of cointegration vectors and the likelihood ratio test of the hypothesis that it has a given number

Missing data: Our view of the state of the art

by Joseph L. Schafer, John W. Graham - Psychological Methods , 2002
"... Statistical procedures for missing data have vastly improved, yet misconception and unsound practice still abound. The authors frame the missing-data problem, review methods, offer advice, and raise issues that remain unresolved. They clear up common misunderstandings regarding the missing at random ..."
Abstract - Cited by 739 (1 self) - Add to MetaCart
at random (MAR) concept. They summarize the evidence against older procedures and, with few exceptions, dis-courage their use. They present, in both technical and practical language, 2 general approaches that come highly recommended: maximum likelihood (ML) and Bayes-ian multiple imputation (MI). Newer

Markov Random Field Models in Computer Vision

by S. Z. Li , 1994
"... . A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model. The l ..."
Abstract - Cited by 516 (18 self) - Add to MetaCart
. A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The posterior probability is usually derived from a prior model and a likelihood model

Minimum Error Rate Training in Statistical Machine Translation

by Franz Josef Och , 2003
"... Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training cri ..."
Abstract - Cited by 757 (7 self) - Add to MetaCart
Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training

Probabilistic Principal Component Analysis

by Michael E. Tipping, Chris M. Bishop - JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B , 1999
"... Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation of paramet ..."
Abstract - Cited by 709 (5 self) - Add to MetaCart
Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation

Additive Logistic Regression: a Statistical View of Boosting

by Jerome Friedman, Trevor Hastie, Robert Tibshirani - Annals of Statistics , 1998
"... Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input dat ..."
Abstract - Cited by 1750 (25 self) - Add to MetaCart
data, and taking a weighted majority vote of the sequence of classifiers thereby produced. We show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles, namely additive modeling and maximum likelihood. For the two-class problem, boosting can
Results 11 - 20 of 22,593