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Description: Generalized Method of Moments(GMM) 广义矩方法，是用来估计模型参数的计量经济学过程。具体说明见其中的gmmdoc.pdf。-Generalized Method of Moments (GMM) GMM method is used to estimate model parameters Econometrics process. See which specify gmmdoc.pdf.
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Size: 553984 |
Author: yangchengbo |
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Description: GMM工具箱第一部分。包含了GMM算法的各个函数，对GMM建模很有帮助.-GMM first part of the toolbox. GMM algorithm contains all functions of GMM modeling helpful.
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Size: 104448 |
Author: chenjie |
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Description: svm和hmm混合模型手写签名认证,充分利用SVM的分类能力以及HMM适合处理连续信号的优势进行手写签名认证-hmm mixed model and svm handwritten signature authentication, make full use of the classification ability of SVM and HMM for continuous signal processing advantages of the handwritten signature verification
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Size: 4217856 |
Author: yqch |
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Description: GMM很好的理论资料,对高斯模型的详细描述以及EM算法的介绍。对编程有一定的帮助。-This is for the initial researcher to study about the GMM Model.
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Size: 195584 |
Author: 郭伟 |
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Description: Expectation Maximization for training GMM s, full covariances. Requires vqtrain.m to have a good initialization.
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Size: 2048 |
Author: Parvatishankar |
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Description: GMM是语音识别中比较成熟的算法之一，这个MATALB完整的实现了基于GMM算法的识别训练程序，可以适用于GMM识别和学习的同行的参考-GMM is a speech recognition algorithm for the more mature one, this MATALB full implementation of the identification algorithm based on GMM training procedures, can be applied to GMM counterparts to identify and study the reference
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Size: 2048 |
Author: yilingzhu |
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Description: GMM说话人识别平台全套，从特征提取到模式识别，到识别率，一整套都有-GMM speaker recognition platform full, from feature extraction to pattern recognition, the recognition rate, set both
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Size: 20825088 |
Author: 周虎 |
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Description: 本文以k-means算法为背景，引入信息熵相关知识，从而实现全自动分割图像。然而在利用混合高斯模型对图像进行数据分析时，会发生一定的过拟合现象，导致我们得不到预期的聚类数目。本文设计合理的合并准则，令模型简化，有效地消除过拟合现象，使得最后得到的聚类数目与预期符合。，设计合理的准则改进了图像的全自动分割方法，使得分割结果更加优化(In this paper, k-means algorithm is used as the background, and information entropy related knowledge is introduced to realize full-automatic image segmentation. However, when the Gaussian mixture model is used to analyze the image data, there will be some over-fitting phenomenon, resulting in that we cannot get the expected number of clusters. In this paper, a reasonable merging criterion is designed to simplify the model and effectively eliminate the over-fitting phenomenon, so that the final clustering number is in line with the expectation. A reasonable criterion is designed to improve the automatic image segmentation method and make the segmentation result more optimized.)
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Size: 1024 |
Author: xiaoxiaofish |
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