Base Noun Phrase translation using web data and the EM algorithm

Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm

Within the context of emission tomography, we study volumetric reconstruction methods based on the Expectation Maximization (EM) algorithm. We show, for the first time, the equivalence of the standard implementation of the EM‐based reconstruction with an implementation based on hardware‐accelerated volume rendering for nearest‐neighbor (NN) interpolation. This equivalence suggests that higher‐order kernels should be used with caution and do not necessarily lead to better performance. We also show that the EM algorithm can easily be adapted for different lattices, the body‐centered cubic (BCC) one in particular. For validation purposes, we use the 3D version of the Shepp‐Logan synthetic phantom, for which we derive closed‐form analytical expressions of the projection data. The experimental results show the theoretically‐predicted optimality of NN interpolation in combination with the EM algorithm, for both the noiseless and the noisy case. Moreover, reconstruction on the BCC lattice leads to superior accuracy, more compact data representation, and better noise reduction compared to the Cartesian one. Finally, we show the usefulness of the proposed method for optical projection tomography of a mouse embryo.

In Recent days Semi supervised image segmentation techniques play a noteworthy role in image processing. Semi supervised image segmentation needs both labeled data and unlabeled data. It means that a Small amount of human assistance or Prior information is given during clustering process. This paper discusses an enhanced semi supervised image segmentation method from labeled image. It uses both a background selection marker and fore ground object selection marker separately. The EM (Expectation Maximization) algorithm is used for clustering along with must link constraints. The proposed method is applied for natural images using MATLAB 7. Thus the proposed method extracts Object of Interest (OOI) from OONI (Object of Not Interest) efficiently and the experimental results are compared with Standard K Means and EM Algorithm also. The results show that the proposed system gives better results than the other two methods. It may also be suitable for object extraction from natural images and medical image analysis.

An image fusion method is proposed based on a kind of estimation theory EM (expectation maximization) algorithm and discrete wavelet frame for merging multiple sensor images. The registered images are firstly decomposed by discrete wavelet frame. Secondly since the fusion of the lowest frequency band is crucial to the final result, they are fused effectively by EM algorithm. The high frequency bands are fused by selection method involving the informative importance measure. Then the fused image is achieved by the discrete wavelet frame invert transform. Experimental results indicate that the proposed method outperforms the discrete wavelet transform and the existing image fusion methods.

Electronic health records (EHRs) are the records containing the patients' clinic information. The EHRs have been widely used in disease diagnosis and therapy due to the numerous and valuable medical information in them. However, the missing data problem of EHRs hinders the usage. Replacing the missing data with mean values is an approach of data imputation. But, that method weakens the feature importance. In this study, we use the expectation-maximization (EM) algorithm to impute the missing data in EHRs. Some machine learning models, including artificial neural network, logistic regression, support vector machine, and random forests are used to evaluate the effectiveness of data imputation. The experimental results show that the prediction accuracies of cancers by using those models on the EHRs imputed by EM algorithm are higher than those by mean values, which indicates the EM algorithm is able to provide accurate estimations in data imputation of EHRs.

A review of existing multilingual TTS (text-to-speech) systems shows that the secondary language inserted into the primary language sounds more like isolated individual words in an alien language environment and not congruous with the primary language's prosody. Since the letter-by-letter spelling of English words or acronyms appears in Chinese speech quite often, a duration modeling approach for English letters embedded in Chinese speech is proposed to make the English congruous with the primary language's tempo. It takes several major factors as additive factors and estimates all model parameters by an EM (expectation-maximization) algorithm. Experimental results showed that the standard deviation of the duration from the test set was greatly reduced from 59.82 to 9.37 ms by the duration modeling while eliminating effects from factors. The root mean squared error between the original and estimated durations was 9.35 ms for the open tests. Experimental results have confirmed its effectiveness on isolating several main factors that seriously affects the duration. Moreover, the estimated value of the factors agreed well to our prior linguistic knowledge. Besides, the hidden state labels produced by the EM algorithm were linguistically meaningful.

In the scope of image processing expectation maximization (EM) algorithm takes conspicuous place among the other clustering techniques. EM algorithm is suitable for multidimensional data but it requires a number of clusters to be defined a priori that might be a problem for particular applications. The main aim of this paper is to provide time effective EM clustering modification in the case of the unknown number of clusters and multidimensional input. Our work is based on statistical histogram based expectation maximization algorithm (SHEM) proposed by Yang and Huang with the predefined number of clusters. This method utilizes the histogram to provide EM iterations. However, the estimation of the histogram becomes time consuming task with the increase of input data dimension. Our algorithm extends the use of SHEM algorithm by means of a hierarchical histogram data structure, which allows us to reduce the computational load in the multidimensional case as well as to provide an initialization in the case of the unknown number of clusters. The paper includes several experimental results demonstrating the advantages and the disadvantages of the proposed solution

Dataset with missing values is quite common in naive bayesian classifier applications, which affects the capability of classifier. And handling missing values has become a research hot issue in the classification field. EM algorithm, a method of iteration, has been widely applied to statistical inferences involving incomplete data such as missing data, censoring data, group data and data bearing disgusting parameters. This paper introduces EM algorithm. To deal with the defects of EM algorithm's slow convergence speed and local convergence. Forward sampling is introduced into EM algorithm. First, get hold of the swatch using forward sampling; then compute the expectation of missing data in the sample; the expectation is used as the initialization in EM algorithm. Finally, the experiment validates the improved EM algorithm is better than conventionality EM algorithm.

Predicting neuron growth is valuable to understand the morphology of neurons, thus it is helpful in the research of neuron classification. This study sought to propose a new method of predicting the growth of human neurons using 1 907 sets of data in human brain pyramidal neurons obtained from the website of NeuroMorpho.Org. First, we analyzed neurons in a morphology field and used an expectation-maximization algorithm to specify the neurons into six clusters. Second, naive Bayes classifier was used to verify the accuracy of the expectation-maximization algorithm. Experiment results proved that the cluster groups here were efficient and feasible. Finally, a new method to rank the six expectation-maximization algorithm clustered classes was used in predicting the growth of human pyramidal neurons.

Word reordering is one of the fundamental problems of machine translation. It is an important factor in the quality and efficiency of machine translations. Tackling the reordering problem can lead to significant improvements in translation quality. A new method is introduced with the objective of solving the reordering problem. It exploits sophisticated syntactic-based features for re-ranking the n -best translation candidates provided by a phrase-based statistical machine translation system. These sophisticated reordering features are based on an innovative structure named the phrasal dependency tree, which is inspired from target-side dependency relations among contiguous non-syntactic phrases. The features benefit from phrase dependencies, translation orientation, and distance. A translation candidate is modelled as a directed and weighted graph built from information provided by the reordering features and is re-scored by the proposed re-ranking system. This system markedly improves the outputs of the machine translation of two syntactically divergent language pairs. The performance is evaluated for Persian→English and German→English translation tasks using the WMT07 benchmark. The results report 0.566/0.95/0.011- and 0.75/0.97/0.024-point improvements in terms of BLEU/TER/LRSCORE metrics on Persian→English and German→English translation tasks, respectively. The superiority of the proposed system in terms of precision and recall measures is demonstrated as well.

Weakly supervised semantic segmentation based on EM algorithm with localization clues

The Probabilistic RBF (PRBF) network constitutes an adaptation of the RBF network for classification. Moreover it extends the typical mixture model by allowing the sharing of mixture components among all classes, in contrast to the conventional approach that suggests mixture components describing only one class. The typical learning method of PRBF for a classification task employs the Expectation – Maximization (EM) algorithm. This widely used method depends strongly on the initial parameter values. The Greedy EM algorithm is a recently proposed method that tries to overcome this drawback, in the case of the density estimation problem using mixture models. In this work we propose a similar approach for incremental training of the PRBF network for classification. The proposed algorithm starts with a single component and incrementally adds more components. After convergence the algorithm splits all the components of the network. The addition of a new component is based on criteria for detecting a region in the data space that is crucial for the classification task. Experimental results using several well-known classification datasets indicate that the incremental method provides solutions of superior classification performance.

Ensemble learning, the machine learning paradigm where multiple models are combined, has exhibited promising perfomance in a variety of tasks. The present work focuses on unsupervised ensemble classification. The term unsupervised refers to the ensemble combiner who has no knowledge of the ground-truth labels that each classifier has been trained on. While most prior works on unsupervised ensemble classification are designed for independent and identically distributed (i.i.d.) data, the present work introduces an unsupervised scheme for learning from ensembles of classifiers in the presence of data dependencies. Two types of data dependencies are considered: sequential data and networked data whose dependencies are captured by a graph. For both, novel moment matching and Expectation-Maximization algorithms are developed. Performance of these algorithms is evaluated on synthetic and real datasets, which indicate that knowledge of data dependencies in the meta-learner is beneficial for the unsupervised ensemble classification task.

<p><strong> </strong>It is practically impossible for pure machine translation approach to process all of translation problems; however, Rule Based Machine Translation and Statistical Machine translation (RBMT and SMT) use different architectures for performing translation task. Lexical analyser and syntactic analyser are solved by Rule Based and some amount of ambiguity is left to be solved by Expectation–Maximization (EM) algorithm, which is an iterative statistic algorithm for finding maximum likelihood. In this paper we have proposed an integrated Hybrid Machine Translation (HMT) system. The goal is to combine the best properties of each approach. Initially, Arabic text is keyed into RBMT; then the output will be edited by EM algorithm to generate the final translation of English text. As we have seen in previous works, the performance and enhancement of EM algorithm, the key of EM algorithm performance is the ability to accurately transform a frequency from one language to another. Results showing that, as proved by BLEU system, the proposed method can substantially outperform standard Rule Based approach and EM algorithm in terms of frequency and accuracy. The results of this study have been showed that the score of HMT system is higher than SMT system in all cases. When combining two approaches, HMT outperformed SMT in Bleu score.</p>

In this paper, an unsupervised image segmentation algorithm is proposed, which combines spatial constraints with a kernel fuzzy c-means (KFCM) clustering algorithm. Conventional KFCM clustering segmentation algorithm does not incorporate the spatial context information of image, which makes it sensitive to the noise and intensity variations. In order to overcome the shortcomings, the contents of image is characterized by Gaussian mixture model, and the parameters of model are estimated by modified expectation maximization (EM) algorithm, which overcomes the classical EM algorithm drawbacks that easily trap in local maxima and be susceptible to initial value. According to the maximum a posterior theorem, we can get the pixel maximum posterior probability. We redefine the objective function of the KFCM algorithm which incorporates the pixel maximum posteriori probability, by minimizing the fuzzy objective function, the fuzzy segmentation algorithm is derived. The experimental results on a synthetic image and a real magnetic resonance image show that the proposed algorithm is more effective than the conventional FCM algorithm without local spatial constraints.