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An adjoint for likelihood maximization
 Proceedings of the Royal Society A: Mathematics
, 2009
"... The process of likelihood maximization can be found in many different areas of computational modelling. However, the construction of such models via likelihood maximization requires the solution of a difficult multimodal optimization problem involving an expensive O(n3) factorization. The optimizat ..."
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Cited by 3 (0 self)
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The process of likelihood maximization can be found in many different areas of computational modelling. However, the construction of such models via likelihood maximization requires the solution of a difficult multimodal optimization problem involving an expensive O(n3) factorization
Likelihood Maximization on Phylogenetic Trees
, 2010
"... We consider the problem of reconstructing the root ancestral state for a binary character on a fixedtopology binary phylogenetic tree, and compare the methods of maximum parsimony and maximum likelihood with the goal of checking if the methods are in agreement. For the likelihood method, we conside ..."
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We consider the problem of reconstructing the root ancestral state for a binary character on a fixedtopology binary phylogenetic tree, and compare the methods of maximum parsimony and maximum likelihood with the goal of checking if the methods are in agreement. For the likelihood method, we
Visual Tracking by Weighted Likelihood Maximization
"... Abstract—A probabilistic real time tracking algorithm is proposed. The distribution of the target is represented by a Gaussian mixture model (GMM) and the weighted likelihood of the target is maximized in order to localize it in an image sequence. The role of the weight is important as it allows gra ..."
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Abstract—A probabilistic real time tracking algorithm is proposed. The distribution of the target is represented by a Gaussian mixture model (GMM) and the weighted likelihood of the target is maximized in order to localize it in an image sequence. The role of the weight is important as it allows
Quadratic weighted automata: Spectral algorithm and likelihood maximization
 Journal of Machine Learning Research
"... In this paper, we address the problem of nonparametric density estimation on a set of strings Σ∗. We introduce a probabilistic model – called quadratic weighted automaton, or QWA – and we present some methods which can be used in a density estimation task. A spectral analysis method leads to an eff ..."
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Cited by 7 (0 self)
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to an effective regularization and a consistent estimate of the parameters. We provide a set of theoretical results on the convergence of this method. Experiments show that the combination of this method with likelihood maximization may be an interesting alternative to the wellknown BaumWelch algorithm.
SAMPLE ITERATIVE LIKELIHOOD MAXIMIZATION FOR SPEAKER VERIFICATION SYSTEMS
"... Gaussian Mixture Models (GMMs) have been the dominant technique used for modeling in speaker recognition systems. Traditionally, the GMMs are trained using the Expectation Maximization (EM) algorithm and a large set of training samples. However, the convergence of the EM algorithm to a global maximu ..."
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maximum is conditioned on proper parameter initialization, a large enough training sample set, and several iterations over this training set. In this work, a Sample Iterative Likelihood Maximization (SILM) algorithm based on a stochastic descent gradient method is proposed. Simulation results showed
Hierarchical POMDP Controller Optimization by Likelihood Maximization
"... Planning can often be simplified by decomposing the task into smaller tasks arranged hierarchically. Charlin et al. (2006) recently showed that the hierarchy discovery problem can be framed as a nonconvex optimization problem. However, the inherent computational difficulty of solving such an optimi ..."
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Cited by 32 (3 self)
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such an optimization problem makes it hard to scale to realworld problems. In another line of research, Toussaint et al. (2006) developed a method to solve planning problems by maximumlikelihood estimation. In this paper, we show how the hierarchy discovery problem in partially observable domains can be tackled
MAP Estimation for Graphical Models by Likelihood Maximization
"... Computing a maximum a posteriori (MAP) assignment in graphical models is a crucial inference problem for many practical applications. Several provably convergent approaches have been successfully developed using linear programming (LP) relaxation of the MAP problem. We present an alternative approac ..."
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Cited by 5 (2 self)
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approach, which transforms the MAP problem into that of inference in a mixture of simple Bayes nets. We then derive the Expectation Maximization (EM) algorithm for this mixture that also monotonically increases a lower bound on the MAP assignment until convergence. The update equations for the EM algorithm
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
, 2003
"... The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction methods. We describe a new approach, based on the maximumlikelihood principle, which clearly satisfies these requirements. The ..."
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Cited by 2182 (27 self)
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of distancebased and parsimony approaches. The reduction of computing time is dramatic in comparison with other maximumlikelihood packages, while the likelihood maximization ability tends to be higher. For example, only 12 min were required on a standard personal computer to analyze a data set consisting
Likelihoodmaximizing beamforming for robust handsfree speech recognition
 IEEE Trans. Speech, & Audio Process
, 2004
"... Abstract—Speech recognition performance degrades significantly in distanttalking environments, where the speech signals can be severely distorted by additive noise and reverberation. In such environments, the use of microphone arrays has been proposed as a means of improving the quality of capture ..."
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Cited by 33 (4 self)
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the goal of the array processing is not to generate an enhanced output waveform but rather to generate a sequence of features which maximizes the likelihood of generating the correct hypothesis. In this approach, called likelihoodmaximizing beamforming, information from the speech recognition system
Results 1  10
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