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Results 1 - 10
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39,325
Maximum likelihood from incomplete data via the EM algorithm
- JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract
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Cited by 11972 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
A maximum likelihood approach to continuous speech recognition
- IEEE Trans. Pattern Anal. Machine Intell
, 1983
"... Abstract-Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of sta-tistical models for use in speech recognition. We give special attention to determining the ..."
Abstract
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Cited by 477 (9 self)
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Abstract-Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of sta-tistical models for use in speech recognition. We give special attention to determining
The maximum likelihood approach to complex ICA
- in Proc. ICASSP
, 2006
"... We derive the form of the best non-linear functions for performing independent component analysis (ICA) by maximum likelihood estimation. We show that both the form of nonlinearity and the relative gradient update equations for likelihood maximization naturally generalize to the complex case, and th ..."
Abstract
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Cited by 18 (11 self)
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We derive the form of the best non-linear functions for performing independent component analysis (ICA) by maximum likelihood estimation. We show that both the form of nonlinearity and the relative gradient update equations for likelihood maximization naturally generalize to the complex case
Unifying Maximum Likelihood Approaches in Medical Image Registration
, 1999
"... While intensity-based similarity measures are increasingly used for medical image registration, they often rely on implicit assumptions regarding the imaging physics. The motivation of this paper is to clarify the assumptions on which a number of popular similarity measures rely. After formalizing r ..."
Abstract
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Cited by 90 (22 self)
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registration based on general image acquisition models, we show that the search for an optimal measure can be cast into a maximum likelihood estimation problem. We then derive similarity measures corresponding to different modeling assumptions and retrieve some well-known measures (correlation coefficient
The Maximum Likelihood Approach to Voting on Social Networks
"... Abstract — One view of voting is that voters have inherently different preferences – de gustibus non est disputandum – and that voting is merely a method for reaching a reasonable compromise solution. An alternative view is that some of the alternatives really are better in an objective sense, and b ..."
Abstract
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Cited by 2 (0 self)
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, the function that takes the votes as input and produces the outcome that maximizes the likelihood of these votes as output. We will first review some of the work on the maximum likelihood approach to voting. Most of this work supposes that, conditional on the correct outcome, votes are independent. In reality
A Maximum-Likelihood Approach to Visual Event Classification
- In Proceedings of the Fourth European Conference on Computer Vision
"... This paper presents a novel framework, based on maximum likelihood, for training models to recognise simple spatial-motion events, such as those described by the verbs pick up, put down, push, pull, drop, and throw, and classifying novel observations into previously trained classes. The model th ..."
Abstract
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Cited by 51 (11 self)
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This paper presents a novel framework, based on maximum likelihood, for training models to recognise simple spatial-motion events, such as those described by the verbs pick up, put down, push, pull, drop, and throw, and classifying novel observations into previously trained classes. The model
A maximum likelihood approach for selecting sets of alternatives
- In Proceedings of the 28th Annual Conference on Uncertainty in Artificial Intelligence (UAI
, 2012
"... We considerthe problem of selecting a subset of alternatives given noisy evaluations of the relative strength of different alternatives. We wish to select a k-subset (for a given k) that provides a maximum likelihood estimate for one of several objectives, e.g., containing the strongest alternative. ..."
Abstract
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Cited by 20 (9 self)
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We considerthe problem of selecting a subset of alternatives given noisy evaluations of the relative strength of different alternatives. We wish to select a k-subset (for a given k) that provides a maximum likelihood estimate for one of several objectives, e.g., containing the strongest alternative
A Context Sensitive Maximum Likelihood Approach to Chunking
- IN: PROCEEDINGS OF CONLL-2000 AND LLL-2000
, 2000
"... ..."
A Maximum-Likelihood Approach to Modeling Multisensory Enhancement
, 2001
"... Multisensory response enhancement (MRE) is the augmentation of the response of a neuron to sensory input of one modality by simultaneous input from another modality. The maximum likelihood (ML) model presented here modi es the Bayesian model for MRE (Anastasio et al.) by incorporating a decisio ..."
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Cited by 7 (4 self)
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Multisensory response enhancement (MRE) is the augmentation of the response of a neuron to sensory input of one modality by simultaneous input from another modality. The maximum likelihood (ML) model presented here modi es the Bayesian model for MRE (Anastasio et al.) by incorporating a
Team Localization: A Maximum Likelihood Approach
, 2001
"... This paper describes a method for localizing the members of a mobile robot team by using only the robots themselves as landmarks. That is, we describe a method whereby each robot can determine the relative range, bearing and orientation of every other robot in the team, without the use of GPS, exter ..."
Abstract
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Cited by 5 (1 self)
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(such sensors can be constructed using cameras or scanning laser range-finders). By employing a combination of maximum likelihood estimation and numerical optimization, we can subsequently infer the relative pose of every robot in the team at any given point in time.
Results 1 - 10
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39,325
