We present an iterative technique to generate phrase tables for SMT, which is based on force-aligning the training data with a modified translation decoder. Different from previous work, we completely avoid the use of a word alignment or phrase extraction heuristics, moving towards a more principled phrase generation and probability estimation. During training, we allow the decoder to generate ...

Generative moment matching network (GMMN), which is based on the maximum mean discrepancy (MMD) measure, is a generative model for unsupervised learning, where the mini-batch stochastic gradient descent is applied for the update of parameters. In this work, instead of obtaining a mini-batch randomly, each mini-batch in the iterations is selected in a submodular way such that the most informativ...

In speech synthesis with sparse training data, phonetic decision trees are frequently used for balance between model complexity and available data. The traditional training procedure is that decision trees are constructed after parameters for each phones optimized in the EM algorithm. This paper proposes an iterative re-optimization algorithm in which the decision tree is re-learned after every...

In conventional method, distributed support vector machines (SVM) algorithms are trained over pre-configured intranet/internet environments to find out an optimal classifier. These methods are very complicated and costly for large datasets. Hence, we propose a method that is referred as the Cloud SVM training mechanism (CloudSVM) in a cloud computing environment with MapReduce technique for dis...

In this paper, two new hybrid algorithms are proposed for decoding Low Density Parity Check (LDPC) codes. Original version of the proposed algorithms named Search Based Weighted Multi Bit Flipping (SWMBF). The main idea of these algorithms is flipping variable multi bits in each iteration, change in which leads to the syndrome vector with least hamming weight. To achieve this, the proposed algo...

Typically, parameter estimation for a hidden Markov model (HMM) is performed using an expectation-maximization (EM) algorithm with the maximum-likelihood (ML) criterion. The EM algorithm is an iterative scheme which is well-deened and numerically stable, but convergence may require a large number of iterations. For speech recognition systems utilizing large amounts of training material, this re...

in this paper, the approximate solution of the differential system modeling hiv infection of cd4+ t cells isobtained by a reliable algorithm based on an adaptation of the standard variational iteration method (vim), which is called the multi-stage variational iteration method(msvim). a comparison between msvim and the fourthorder runge-kutta method (rk4-method) reveal that the proposed techniqu...

In this paper, by using the exponential convexity property of a barrier function, we propose an infeasible interior-point method for Cartesian P_*(k) horizontal linear complementarity problem over symmetric cones. The method uses Nesterov and Todd full steps, and we prove that the proposed algorithm is well define. The iteration bound coincides with the currently best iteration bound for the Ca...

In this paper, first we use an example to show the efficiency of $M$ iteration process introduced by Ullah and Arshad [4] for approximating fixed points of Suzuki generalized nonexpansive mappings. Then by using $M$ iteration process, we prove some strong and $Delta -$convergence theorems for Suzuki generalized nonexpansive mappings in the setting of $CAT(0)$ Spaces. Our results are the extensi...