Some methods have been presented in M3 network, such as randomly division, hyper-planes division, spectral clustering and so on.

Our two improved spectral clustering algorithms use the limited computing resources ( e.g.: memory, CPU) to maximize the accuracy of spectral clustering.

本文提出的两个算法能够在有限的计算资源（例如：CPU和内存）的条件下最大化谱聚类的聚类精确度。

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Spectral clustering method is used to detect the community structure in the PPI network.

利用谱聚类算法来划分PPI网络中的社团结构。

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Firstly, on bisecting K-means is used to quantize image roughly and then we refine the image by improved spectral clustering based weighted distance.

首先利用高效的二分K均值聚类进行粗略量化，然后使用基于加权距离的谱聚类进行再次量化。

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A spectral clustering algorithm based on fuzzy K-harmonic means

基于模糊K-Harmonic Means的谱聚类算法

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Spectral clustering is one of the most popular modern clustering algorithms.

谱聚类算法近几年已经成为当前最流行的聚类算法之一。

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In this thesis, we propose an adaptive spectral clustering ( ASC) algorithm which can effectively deal with the parameter selection problem.

为此，本文提出了一种自适应谱聚类算法（ASC），试图解决谱聚类中的参数选取问题。

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An inter-speaker information ANN method is proposed, analyzing GMM model speaker identification system performance, and a hybrid GMM/ ANN speaker identification system is structured.

通过分析GMM（高斯混合模型）的说话人辨认系统的性能，提出了一种捕捉不同说话人交互信息的人工神经网络（ANN）方法，构成一个GMM/ANN混合说话人辨认系统。

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They are Vector Quantization ( VQ), Hidden Markov Models ( HMM) and Gaussian Mixture Models ( GMM) speaker identification respectively.

采用的方法分别是矢量量化（VQ）识别方法、隐马尔可夫模型（HMM）识别方法、高斯混合模型（GMM）识别方法。

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Gaussian Mixture Model ( GMM) is used to approximate the pdf of sources and results in a practical Expectation Maximum ( EM) algorithm.

采用高斯混合模型（GMM）来逼近源信号的概率密度函数，简化了算法中的积分，导出了一种实用的期望最大算法（EM）算法迭代式。

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To avoid deforming contours directly in a vector-valued space, a Gaussian mixture model ( GMM) is used to describe the statistical distribution of the space and get the boundary and region probabilities.

为了避免直接在多维空间中应用曲线演化模型，采用高斯混合模型（Gaussian mixture model，简称GMM）来描述该特征图像的概率分布，再从分布模型中计算得到每个像素点的区域信息和边界信息。

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GMM is one of the best pattern recognition techniques because of its good performance, simpleness and lower degree of complexity.

说话人识别中有许多先进有效的识别技术，其中高斯混合模型（GMM）由于性能较好、复杂度小、方法简单，是目前最好的说话人识别算法之一。

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Gaussian Mixed Model Based NSCT Remote Sensing Image Fusion

基于高斯混合模型的NSCT变换遥感图像融合

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This paper will use Gaussian Mixed Model to fit image histogram and EM algorithm to estimate Gaussian parameters, thus to segment an image into some regions.

通过高斯混合模型拟合图像直方图，使用EM算法估计高斯参数，进而将图像分割成区域。

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In algorithm ways, Gaussian mixture model ( GMM) is the most successful speaker recognition model at present.

在算法方面，高斯混合模型（GMM）是目前最成功的一种说话人识别模型。

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Gaussian mixture model ( Gaussian Mixture Models, GMM) is an important method of machine learning, which aims to establish a probability model for the target data set.

高斯混合模型（Gaussian mixture models，GMM）是一种重要的机器学习方法，其目的是建立目标数据集的概率模型。

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This paper focuses on the speaker recognition system based on Mel-Frequency Cepstrum Coefficients ( MFCC) and Gaussian Mixture Model ( GMM).

在众多的说话人识别技术中，本文主要研究了基于Mel频率倒谱系数（Mel-Frequency Cepstrum Coefficients，简称MFCC）和高斯混合模型（Gaussian mixture model，简称为GMM）的说话人识别系统。

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Spectrum envelope of high-band speech is obtained from the LSF parameters of narrow-band through GMM.

利用高斯混合模型（GMM）由窄带语音的LSF参数扩展得到高带语音的包络谱信息；

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We adopt the output of Gabor filters as feature and Gaussian mixture model ( GMM) as the classifier.

本文采用Gabor滤波器的输出结果作为特征，高斯混合模型（GMM）作为分类器对织物瑕疵分类。

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GMM-based two-stage Mel-warped Wiener filter.

基于高斯混合模型的两级Mel弯曲维纳滤波。

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When the noise is severe such as 9%, the segmentation accuracy of K-means clustering algorithm, fuzzy C means and method based on Gaussian mixture model decrease 4% averagely.

当噪声程度达到9%时，K均值法、模糊C均值法和高斯混合模型的分割准确率平均下降了4%。

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