Description: 完成两路带有三差信号的拟合输出，算法应用了高斯求逆的方法-Complete two-way with three fitting poor signal output, the algorithm applied the method of inverse Gaussian Platform: |
Size: 13312 |
Author:王真 |
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Description: 附件中的m代码包括了一系列的拟合函数，这些函数通常的输入是概率分布的样本。还有最大似然估计仿真器、最小平方仿真器、混合高斯分布估计的EM算法、-M in the annex to the code, including a series of fitting function, which function normally is the probability distribution of input samples. There are maximum likelihood estimation simulator, emulator least squares, mixed-Gaussian distribution is estimated that the EM algorithm, Platform: |
Size: 68608 |
Author:liyuedsg |
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Description: 多种概率分布的拟合函数集合
本算法包括最大似然估计，最小二乘估计，基于EM算法的多种混合高斯分布估计，EM算法测试实例，绘制每种分布的plot函数。非常有参考价值！-Fitting a wide range of probability distribution functions including the collection of this algorithm maximum likelihood estimation, least squares estimation, based on the EM algorithm estimate a mixture Gaussian distribution, EM algorithm for the test examples, each mapping the distribution of the plot function. Have reference value is! Platform: |
Size: 21504 |
Author:lcp |
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Description: 估计一个离散点云中某个点的高斯曲率和离散曲率，算法是拟合一个二次曲面-Estimate a discrete point cloud to a point in the discrete Gaussian curvature and the curvature, the algorithm is a quadratic surface fitting Platform: |
Size: 1024 |
Author:yanmohan |
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Description: 提出了基于Contourlet变换的数字图像水印算法。与小波变换不同的是，Contourlet变换采用类似于线段
（contour segment）的基得到一种多分辨、局部化、方向性的图像表示。水印信号通过基于内容的乘性方案加载
到 Contourlet 变换系数。在采用零均值广义高斯分布拟合 Contourlet 变换系数的基础上，提出采用极大似然估计
实现水印的盲检测。依据 Neyman-Pearson 准则，在给定虚警率的情况下对判决准则进行了优化。实验结果表明
在保证水印隐蔽性的前提下，水印对常见的信号处理手段以及几何变换具有很好的稳健性-Contourlet transform is proposed based on digital image watermarking algorithm. The difference is that with the wavelet transform, Contourlet transformation similar to that of line (contour segment) of the base by a multi-resolution, localization, directional image representation. Watermark-based content of the program is loaded into Contourlet by transform coefficients. The introduction of zero-mean generalized Gaussian distribution fitting Contourlet transform coefficient based on maximum likelihood estimation proposed by the blind watermark detection. Based on Neyman-Pearson criterion, the false alarm rate in a given case to the sentencing guidelines were optimized. The results show that the watermark hidden in the guarantee under the premise of the watermark signal processing means of the common geometric transformations and is robust Platform: |
Size: 134144 |
Author:betty |
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Description: 数值算法，高斯和拟合，能够对输入的数据进行拟合，并且能够得出最佳的图形，非常值得下载。-Numerical algorithm, Gaussian, and fitting, to fit the input data, and can come to the best of the graphics, well worth the download. Platform: |
Size: 1024 |
Author:FSQ |
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Description: 5个程序包括1，用高斯消去法解线性方程组的算法程序；2，判断k之前的质数并输出；3动态建立多维数组；4，利用点类进行线性拟合；5改进后的人员信息管理系统-Five programs include 1, with the gaussian elimination method of solving linear equations algorithm procedures 2, the prime Numbers and before judge k output 3, dynamic multidimensional array. 4, use some kind of linear fitting 5, the improved personnel information management system
Platform: |
Size: 5287936 |
Author:aaa |
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Description: 混合高斯模型算法，可以产生混合高斯分布，并通过自拟和确定高斯函数个数。-Gaussian mixture model algorithm, can produce a mixture of Gaussian distribution, and through self-fitting to determine the number of Gaussian function. Platform: |
Size: 1024 |
Author: |
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Description: 上传的资料包括c语言设计的高斯消元法，RSSI方法测距跟踪处理，排序法，c实现的拟合算法，利用左边算最小二乘法系数-Upload information, including c language design Gaussian elimination, RSSI methods ranging tracking processing, sorting method, c achieve fitting algorithm, using the least squares method calculation coefficient left Platform: |
Size: 1880064 |
Author:刘欣 |
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Description: Just like EM of Gaussian Mixture Model, this is the EM algorithm for fitting Bernoulli Mixture Model.
GMM is useful for clustering real value data. However, for binary data (such as bag of word feature) Bernoulli Mixture is more suitable. Platform: |
Size: 3072 |
Author:lin |
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Description: 对于单输入单输出的系统（Single input single output，SISO）常采用最小二乘方法辨识系统的参数。最小二乘参数估计是一个经典的方法，概念简明，适应范围广，来源于数理统计的回归分析，它能提供一个在最小方差意义上与实验数据最好拟合的模型，在一些情况下，可得到与极大似然法一样好的统计效果，并能很方便地与其它辨识算法建立关系。在一定条件下，最小二乘法参数估计法有最佳的统计特性，即一致的、无偏的和有效的结果。本代码主要关于使用递推最小二乘辨识方法与增广最小二乘辨识方法辨识模型参数，采用高斯噪声作为系统的噪声。(For Single input Single output (SISO), the least squares method is used to identify the parameters of the system.
Least squares parameter estimation is a classic method, concept is concise, wide adaptation, derived from the regression analysis of mathematical statistics, it can provide a minimum variance sense the best fitting model with the experimental data, in some cases, the statistics can be obtained with the maximum likelihood method is as good effect, and can easily establish relations with other identification algorithm.
Under certain conditions, the least square parameter estimation method has the best statistical properties, namely consistent, unbiased and effective results.
This code mainly USES the method of recursive least squares identification method and the augmented least squares identification method to identify the model parameters, using gaussian noise as the noise of the system.) Platform: |
Size: 1024 |
Author:M.R.J.
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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.) Platform: |
Size: 1024 |
Author:xiaoxiaofish |
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Search - gaussian fitting algorithm