Results 1 - 10 of 143,413

Active Appearance Models.

by Timothy F Cootes , Gareth J Edwards , Christopher J Taylor - IEEE Transactions on Pattern Analysis and Machine Intelligence, , 2001
"... AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations ..."
Abstract - Cited by 2154 (59 self) - Add to MetaCart
AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations

A gentle tutorial on the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models

by Jeff A. Bilmes , 1997
"... We describe the maximum-likelihood parameter estimation problem and how the Expectation-form of the EM algorithm as it is often given in the literature. We then develop the EM parameter estimation procedure for two applications: 1) finding the parameters of a mixture of Gaussian densities, and 2) fi ..."
Abstract - Cited by 693 (4 self) - Add to MetaCart
) finding the parameters of a hidden Markov model (HMM) (i.e., the Baum-Welch algorithm) for both discrete and Gaussian mixture observation models. We derive the update equations in fairly explicit detail but we do not prove any convergence properties. We try to emphasize intuition rather than mathematical

Face Recognition Based on Fitting a 3D Morphable Model

by Volker Blanz, Thomas Vetter - IEEE TRANS. PATTERN ANAL. MACH. INTELL , 2003
"... This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D ..."
Abstract - Cited by 551 (19 self) - Add to MetaCart
model, an algorithm to fit the model to images, and a framework for face identification. In this framework, faces are represented by model parameters for 3D shape and texture. We present results obtained with 4,488 images from the publicly available CMU-PIE database and 1,940 images from the FERET

A Model of Investor Sentiment

by Nicholas Barberis, Andrei Shleifer, Robert Vishny - Journal of Financial Economics , 1998
"... Recent empirical research in finance has uncovered two families of pervasive regularities: underreaction of stock prices to news such as earnings announcements, and overreaction of stock prices to a series of good or bad news. In this paper, we present a parsimonious model of investor sentiment, or ..."
Abstract - Cited by 777 (32 self) - Add to MetaCart
, or of how investors form beliefs, which is consistent with the empirical findings. The model is based on psychological evidence and produces both underreaction and overreaction for a wide range of parameter values. � 1998 Elsevier Science S.A. All rights reserved. JEL classification: G12; G14

The Infinite Hidden Markov Model

by Matthew J. Beal, Zoubin Ghahramani, Carl E. Rasmussen - Machine Learning , 2002
"... We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data. Th ..."
Abstract - Cited by 637 (41 self) - Add to MetaCart
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integrate out the infinitely many transition parameters, leaving only three hyperparameters which can be learned from data

Learning probabilistic relational models

by Nir Friedman, Lise Getoor, Daphne Koller, Avi Pfeffer - In IJCAI , 1999
"... A large portion of real-world data is stored in commercial relational database systems. In contrast, most statistical learning methods work only with "flat " data representations. Thus, to apply these methods, we are forced to convert our data into a flat form, thereby losing much ..."
Abstract - Cited by 613 (30 self) - Add to MetaCart
objects. Although PRMs are significantly more expressive than standard models, such as Bayesian networks, we show how to extend well-known statistical methods for learning Bayesian networks to learn these models. We describe both parameter estimation and structure learning — the automatic induction

Hidden Markov models in computational biology: applications to protein modeling

by Anders Krogh, Michael Brown, I. Saira Mian, Kimmen Sjölander, David Haussler - JOURNAL OF MOLECULAR BIOLOGY , 1994
"... Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EF-hand calcium binding moti ..."
Abstract - Cited by 655 (39 self) - Add to MetaCart
Hidden.Markov Models (HMMs) are applied t.0 the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated the on globin family, the protein kinase catalytic domain, and the EF-hand calcium binding

What is a hidden Markov model?

by Sean R. Eddy , 2004
"... Often, problems in biological sequence analysis are just a matter of putting the right label on each residue. In gene identification, we want to label nucleotides as exons, introns, or intergenic sequence. In sequence alignment, we want to associate residues in a query sequence with ho-mologous resi ..."
Abstract - Cited by 1344 (8 self) - Add to MetaCart
splice site consenses, codon bias, exon/intron length preferences, and open reading frame analysis all in one scoring system. How should all those parameters be set? How should different kinds of information be weighted? A second issue is being able to interpret results probabilistically. Finding a best

A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants

by Radford Neal, Geoffrey E. Hinton - Learning in Graphical Models , 1998
"... . The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the d ..."
Abstract - Cited by 993 (18 self) - Add to MetaCart
. The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect

Longitudinal data analysis using generalized linear models”.

by Kung-Yee Liang , Scott L Zeger - 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 ..."
Abstract - Cited by 1526 (8 self) - Add to MetaCart
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
Results 1 - 10 of 143,413