In this paper we proposed new estimators of parameters for a Naive Bayes Classifier based on Beta Distributions. Equations were obtained for these estimators using an em-like algorithm and they provide numerical estim...
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ISBN:
(纸本)9789814619967
In this paper we proposed new estimators of parameters for a Naive Bayes Classifier based on Beta Distributions. Equations were obtained for these estimators using an em-like algorithm and they provide numerical estimates for those parameters. Furthermore, two forms for that Naive Bayes Classifier were presented.
This paper provides a coherent framework for studying longitudinal manifold-valued data for which the dynamic changes over time. We introduce a Bayesian mixed-effects model that allows estimating both a group-represen...
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This paper provides a coherent framework for studying longitudinal manifold-valued data for which the dynamic changes over time. We introduce a Bayesian mixed-effects model that allows estimating both a group-representative piecewise-geodesic trajectory in the Riemannian space of shape and interindividual variability. We prove the existence of the maximum a posteriori estimate and its asymptotic consistency under reasonable assumptions. Due to the nonlinearity of the proposed model, we use a stochastic version of the expectation-maximization algorithm to estimate the model parameters. Our simulations show that our model is not noise-sensitive and succeeds in explaining various paths of progression.
The expectation-maximization (em) algorithm is a powerful computational technique for maximum likelihood estimation in incomplete data models. When the expectation step cannot be performed in closed form, a stochastic...
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The expectation-maximization (em) algorithm is a powerful computational technique for maximum likelihood estimation in incomplete data models. When the expectation step cannot be performed in closed form, a stochastic approximation of em (SAem) can be used. The convergence of the SAem toward critical points of the observed likelihood has been proved and its numerical efficiency has been demonstrated. However, sampling from the posterior distribution may be intractable or have a high computational cost. Moreover, despite appealing features, the limit position of this algorithm can strongly depend on its starting one. Sampling from an approximation of the distribution in the expectation phase of the SAem allows coping with these two issues. This new procedure is referred to as approximated-SAem and is proved to converge toward critical points of the observed likelihood. Experiments on synthetic and real data highlight the performance of this algorithm in comparison to the SAem and the em when feasible. (C) 2020 Elsevier B.V. All rights reserved.
Mixture models appear in many research areas. In genetic and epidemiology applications, sometimes the mixture proportions may vary but are known. For such data, the existing methods for the underlying component densit...
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Mixture models appear in many research areas. In genetic and epidemiology applications, sometimes the mixture proportions may vary but are known. For such data, the existing methods for the underlying component density estimation may produce undesirable results: negative values in the density estimates. In this paper, we propose a maximum smoothed likelihood method to estimate these component density functions. The proposed estimates maximize a smoothed log likelihood function which can inherit all the important properties of probability density functions. A majorization-minimization algorithm is suggested to compute the proposed estimates numerically. We show that, starting from any initial value, the algorithm converges. Furthermore, we establish the asymptotic convergence rate of the L-1 errors of our proposed estimators. Our method provides a general framework for dealing with many similar mixture model problems. An adaptive procedure is suggested for choosing the bandwidths in our estimation procedure. Simulation studies show that the proposed method is very promising and can be much more efficient than the existing method in terms of the L-1 errors. A malaria data application shows the advantages of our method over others.
The frailty model is one of the most popular models used to analyze clustered failure time data, and the frailty term in the model is used to assess associations in each cluster. The frailty model based on the semipar...
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The frailty model is one of the most popular models used to analyze clustered failure time data, and the frailty term in the model is used to assess associations in each cluster. The frailty model based on the semiparametric accelerated failure time model attracts less attention than the one based on the proportional hazards model due to its computational difficulties. In this paper, we develop a new estimation method for the semiparametric accelerated failure time gamma frailty model based on the em-like algorithm and the rank-like estimation method. The proposed method is compared with the existing emalgorithm, which incorporates the M-estimator in the M-step. From simulation studies, we show that the rank-like estimation method in the M-like step simplifies the estimation procedure and reduces the computational time by the linear programming approach. With respect to the accuracy of estimates and length of computational time, the proposed method outperforms the existing estimation methods. For illustration, we apply the proposed method to the data set of sublingual nitroglycerin and oral isosorbide dinitrate on angina pectoris of coronary heart disease patients. (C) 2010 Elsevier B.V. All rights reserved.
The Multiple Target Tracking (MTT) problem is one of the fundamental challenges in computer vision. In this paper, we propose a feasible detection and association based MTT system which uses a modified Deformable Part...
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ISBN:
(纸本)9781479986880
The Multiple Target Tracking (MTT) problem is one of the fundamental challenges in computer vision. In this paper, we propose a feasible detection and association based MTT system which uses a modified Deformable Part-Based Model (DPM) to generate detection results and then links detections into tracklets to further form long trajectories. We first describe our modified DPM algorithm which could automatically discovery optimal object part configurations to improve detection performance. Next to tackle the MTT problem, e.g., associating detections under imperfect detector identifications, severe occlusions and interferences between objects, etc conditions, we introduce an em-like inference algorithm that alternatively optimizes the Trajectory Models (TM) for all the targets and the Maximum A Posterior (MAP) solution of the Markov Random Field (MRF) model. At the E-step, we update the TM based on the inference result of the current MRF model, and at the M-step, we use the up-to-date TM to re-compute the probabilities in the MRF model to re-fine the MAP solution. As shown by our experimental results, the presented detection and association based MTT system leads to satisfactory performance.
The frailty model is one of the most popular models used to analyze clustered failure time data, where the frailty term is used to assess an association within each cluster. The frailty model based on the semiparametr...
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The frailty model is one of the most popular models used to analyze clustered failure time data, where the frailty term is used to assess an association within each cluster. The frailty model based on the semiparametric accelerated failure time model attracts less attention than the one based on the proportional hazards model due to its computational difficulties. In this paper, we relax the frailty distribution to the generalized gamma distribution, which can accommodate most of the popular frailty assumptions. The estimation procedure is based on the em-like algorithm by employing the MCMC algorithm in the E-step and the profile likelihood estimation method in the M-step. We conduct an extensive simulation study and find that there is a significant gain in the proposed method with respect to the estimation of the frailty variance with a slight loss of accuracy in the parameter estimates. For illustration, we apply the proposed model and method to a data set of sublingual nitroglycerin and oral isosorbide dinitrate on angina pectoris of coronary heart disease patients. (C) 2013 Elsevier B.V. All rights reserved.
Implementation of cellular manufacturing systems (CMS) is thriving among manufacturing companies due to many advantages that are attained by applying this system. In this study CMS formation and layout problems are co...
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Implementation of cellular manufacturing systems (CMS) is thriving among manufacturing companies due to many advantages that are attained by applying this system. In this study CMS formation and layout problems are considered. An Electromagnetism like (em-like) algorithm is developed to solve the mentioned problems. In addition the required modifications to make em-like algorithm applicable in these problems are mentioned. A heuristic approach is developed as a local search method to improve the quality of solution of em-like. Beside in order to examine its performance, it is compared with two other methods. The performance of em-like algorithm with proposed heuristic and GA are compared and it is demonstrated that implementing em-like algorithm in this problem can improve the results significantly in comparison with GA. In addition some statistical tests are conducted to find the best performance of em-like algorithm and GA due to their parameters. The convergence diagrams are plotted for two problems to compare the convergence process of the algorithms. For small size problems the performances of the algorithms are compared with an exact algorithm (Branch & Bound). (C) 2011 Elsevier Ltd. All rights reserved.
Portfolio selection is one of the most important problems which human, companies and organizations are in dealing with. Dependency between projects is a major issue, which has not been considered widely according to p...
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Portfolio selection is one of the most important problems which human, companies and organizations are in dealing with. Dependency between projects is a major issue, which has not been considered widely according to previous researches. Dependency often occurs in industrial and constructional projects. In this paper, we developed a model that included cost dependency. Moreover, some technological constraints have been considered. Stochastic revenue and risk are some other major points of the model. An Electromagnetism-like (em-like) and a Genetic algorithm (GA) are developed to solve the proposed model. Some experimental tests are developed to examine the influence of algorithm's parameters on their performance. In addition, the results of the algorithms are reported. Comparison of GA and em-like algorithm with optimum answer shows the efficiency of the algorithms. In addition, it reveals that GA has better performance in comparison with em-like algorithm. (C) 2010 Elsevier Ltd. All rights reserved.
Current technological progress in Hardware, Data Bases and Object Oriented languages implies the manipulation, stock and representation of objects with more and more complex data. The notion of Symbolic Objects is int...
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Current technological progress in Hardware, Data Bases and Object Oriented languages implies the manipulation, stock and representation of objects with more and more complex data. The notion of Symbolic Objects is introduced on the base of Diday's work and the necessity to be adapted to this notion appears for most recent classification methods. The aim of this paper is the adaptation of the classical Bayesian discrimination rule to the Symbolic Objects problematic. This will be performed by the prior probabilities' estimation and by a kernel density estimation.
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