Speech Separation Based on Gaussian Mixture Model Probability Density Function Estimation

Moving Object Detection Algorithm Based on Edge Detection and Gaussian Mixture Model

基于边缘检测和混合高斯模型的运动目标检测算法

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A Range Image Segmentation Algorithm Based on Gaussian Mixture Model

一种基于高斯混合模型的距离图像分割算法

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Gaussian mixture model training method based on genetic algorithms and fuzzy approach

基于遗传模糊高斯混合模型的训练方法

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Study on Speaker Recognition System Based on Gaussian Mixture Model

基于高斯混合模型的说话人识别系统的研究

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However, the commonly used Gaussian mixture model and modeling based on kernel estimation both have their own shortcomings.

而通常所采用的混合高斯模型和基于核估计的方法具有各自的缺点。

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This article optimizes the Gaussian Mixture background modeling algorithm through in-depth study of it, improving the speed of updates, and almost does not change the accuracy.

通过对混过高斯背景建模算法的研究，优化了背景更新算法，在几乎不改变准确率的前提下提高了背景更新的速度，为进一步的操作提供了较好的基础。

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We perform unsupervised classification to hyperspectral image by Gaussian mixture modeling, estimate the parameters of the mixture model using the expectation maximization ( EM) algorithm in merged operations to data after PCA linear projection, and select the number of components according to the proposed criterion.

分类过程中，论文采用期望最大化（Expectation maximization）算法在合并的策略下对PCA变换后的数据求解混合模型，并应用所提出的准则进行模型选择从而确定待分类的类别数。

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Thus, the paper focuses on the following aspects. First, in the extraction of the target vehicle, the paper analyzes some commonly used methods which have Multi-frame averaging method, continuous frame difference method, optical flow method, Gaussian mixture modeling approach.

因而论文着重研究了以下几个方面。首先论文在对目标车辆提取方面，分析了一些常用的方法有多帧平均法、连续帧差法、光流法、高斯混合建模法等。

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For all of the background modeling methods, the Gaussian mixture modeling is considered as a good method with high performance in both detection capability and adaptability. Nevertheless, it is difficult to implemented in real time for its huge computational complexity.

在背景建模各方法中，混合高斯建模方法是公认的检测效果和适应性都较好的方法，但其所需计算量巨大，难以实时实现。

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Further programming of the above four algorithms, simulation results show that Gaussian mixture modeling algorithm is better and more conducive to the transport vehicle segmentation.

进一步通过编程实现上述四种算法，仿真实验结果表明，混合高斯建模算法的分割效果更好，更有利于交通车辆分割。

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Scalable Parameter Learning Algorithm for Gaussian Mixture Modeling

高斯混合模型的数据尺度可压缩参数学习算法

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Structural risk minimization principle based gaussian mixture modeling

基于结构风险最小化准则的高斯混合模型的参数估计

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Then using the combining method of co-association matrix, the final result is obtained by using spectral clustering algorithm on this matrix.

再采用基于互联合矩阵的集成方法，在构造的相似度矩阵上应用谱聚类算法来完成对数据的最后聚类。

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After that, this paper uses spectral clustering on the similarity matrix constructed in the last step to complete the image segmentation.

之后再利用谱聚类对已经构造好的相似度矩阵进行聚类，完成对图像的分割。

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Spectral clustering algorithm is based on spectral graph theory, it can map data to spectral, and it has better robustness.

谱聚类算法根据谱图划分理论，对数据进行谱映射，在任意样本空间有较好的鲁棒性，因此本文采用谱聚类算法来解决传统聚类算法存在的问题。

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Spectral clustering as a class of new and effective clustering method has been widely concerned and successfully applied in image segmentation.

谱聚类作为一类新兴的、有效的聚类方法得到广泛的关注，并已成功的应用于图像分割。

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We analysed the bottleneck of utilizing spectral clustering algorithm on large scale text corpus, and proposed solutions.

本文中分析了谱聚类算法应用到大规模文本聚类中的难点，并给出了解决办法。

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Spectral clustering as a class of new and effective clustering method has been the major research in the field of machine learning.

谱聚类作为一类新兴的、高性能的聚类方法，目前已成为国际上机器学习领域的研究热点。

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To solute these problems, we adapt the combination of Latent Semantic Analysis and Spectral Clustering.

针对这些问题，在第四章提出了结合潜在语义分析的谱聚类算法。

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In addition to the optimization need of the traditional spectral clustering, this paper researches the artificial immune system deeply.

针对传统谱聚类算法的优化需求，本文深入研究和分析了人工免疫系统。

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