Generalized Multiobjective Multitree model (GMMmodel) considering by the first time multitree-multicast load balancing with splitting in a multiobjective context. To solve the GMM-model, a multiobjective evolutionary algorithm (MOEA) inspired by the Strength Pareto Evolutionary Algorithm (SPEA) was proposed. In this paper, we extends the GMM-model to dynamic multicast groups (i.e., in which egr...

This paper analyzes the higher order asymptotic properties of Generalized Method of Moments (GMM) estimators for linear time series models using many lags as instruments. A data dependent moment selection method based on minimizing the approximate mean squared error is developed. In addition, a new version of the GMM estimator based on kernel weighted moment conditions is proposed. It is shown ...

This paper presents some experiments on discriminative training for GMM/UBM based speaker recognition systems. We propose two MMIE adaptation methods for GMM component weights suitable for speaker recognition. The impact on performance of this training methods is compared to the standard weight estimation/adaptation criterion, MLE and MAP on standard GMM based systems and on SVM based systems. ...

We compare the performance of ve algorithms for vector quan-tisation and clustering analysis: the Self-Organising Map (SOM) and Learning Vector Quantization (LVQ) algorithms of Kohonen, the Linde-Buzo-Gray (LBG) algorithm, the MultiLayer Perceptron (MLP) and the GMM/EM algorithm for Gaussian Mixture Models (GMM). We propose that the GMM/EM provides a better representation of the speech space an...

Gaussian Mixture Model (GMM) with Fuzzy c-means attempts to classify signals into speech and music. Feature extraction is done before classification. The classification accuracy mainly relays on the strength of the feature extraction techniques. Simple audio features such as Time domain and Frequency domain are adopted. The time domain features are Zero Crossing Rate (ZCR) and Short Time Energy...

Gaussian mixture models (GMM) have been widely and successfully used in speaker recognition during the last decade. However, they are generally trained using the generative criterion of maximum likelihood estimation. In this paper, we propose a simple and efficient discriminative approach to learn GMM with a large margin criterion to solve the classification problem. Our approach is based on a ...

SUMMARY The impact of a Pseudomonas fluorescens strain, genetically modified for kanamycin resistance and lactose utilisation (the GMM), could be enhanced by soil amendment with lactose and kanamycin. Lactose addition decreased the shoot to root ratio of pea, and both soil amendments increased the populations of total culturable bacteria and the inoculated GMM. Only kanamycin perturbed the bact...