Location:
Search - K-mean
Search list
Description: BAYESGAUSS贝叶斯分类器对高斯模式。
D = BAYESGAUSS(X,钙、马、P)计算贝叶斯决策
n维的功能模式的行X。
n-by-n-by-W大小的CA是一个数组,其中包含了协方差
的矩阵大小n-by-n,W类的数量。
大小n-by-W 马是一个数组,其列。柯尔-
水洼意味着向量。一个赔偿。矩阵和平均向量必须
为每个类指定,即使一些都是平等的。X是大小
K-by-n,K是模式的数量分类。P是
1-by-W数组,包含发生的概率
每个类。如果P不包括在参数、类
假定同样可能。-BAYESGAUSS Bayes classifier for Gaussian patterns.
D = BAYESGAUSS(X, CA, MA, P) computes the Bayes decision
functions of the n-dimensional patterns in the rows of X.
CA is an array of size n-by-n-by-W containing W covariance
matrices of size n-by-n, where W is the number of classes.
MA is an array of size n-by-W, whose columns are the corres-
ponding mean vectors. A cov. matrix and a mean vector must be
specified for each class, even if some are equal. X is of size
K-by-n, where K is the number of patterns to be classified. P is
a 1-by-W array, containing the probabilities of occurrence of
each class. If P is not included in the argument, the classes
are assumed to be equally likely.
Platform: |
Size: 2048 |
Author: 刘晓丹 |
Hits:
Description: k-means clustering is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.-k-means clustering is a method of vector quantization, originally signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Platform: |
Size: 3072 |
Author: lp |
Hits:
Description: 基于K均值的PSO聚类算法,计算多重分形非趋势波动分析matlab程序,均值便宜跟踪的示例。- K-means clustering algorithm based on the PSO, Calculation multifractal detrended fluctuation analysis matlab program, Example tracking mean cheap.
Platform: |
Size: 5120 |
Author: nen |
Hits:
Description: Segmentation with k means clustering and mean shift
Platform: |
Size: 1835008 |
Author: taatyaa |
Hits:
Description: 一个kmean聚类算法的实现，一个很好的示例，仅供大家一起参考和学习-K achieve a mean clustering algorithm, a good example, for your reference and learning together
Platform: |
Size: 1024 |
Author: 方徐伟 |
Hits:
Description: 基于K均值的PSO聚类算法,包含收发两个客户端的链路级通信程序,均值便宜跟踪的示例。- K-means clustering algorithm based on the PSO, Contains two clients receive link-level communications program, Example tracking mean cheap.
Platform: |
Size: 5120 |
Author: yie |
Hits:
Description: 基于K均值的PSO聚类算法,均值便宜跟踪的示例，非归零型差分相位调制信号建模与仿真分析 。- K-means clustering algorithm based on the PSO, Example tracking mean cheap, NRZ type differential phase modulation signal modeling and simulation analysis.
Platform: |
Size: 7168 |
Author: knxaygra |
Hits:
Description: 基于K均值的PSO聚类算法,解耦，恢复原信号，最小均方误差（MMSE）的算法。- K-means clustering algorithm based on the PSO, Decoupling, restore the original signal, Minimum mean square error (MMSE) algorithm.
Platform: |
Size: 5120 |
Author: foumaoheng |
Hits:
Description: 可以直接拿来用 python2.7
在数据挖掘中，K-Means算法是一种 cluster analysis 的算法，其主要是来计算数据聚集的算法，主要通过不断地取离种子点最近均值的算法。(In the data mining, K-Means algorithm is a cluster analysis algorithm, which is mainly to calculate the data aggregation algorithm, mainly through the continuous removal of the seed point of the nearest mean algorithm.)
Platform: |
Size: 1024 |
Author: 向日葵葵wxn
|
Hits:
Description: 有时候我们所获取的图像需进行处理，线性变换也是一种方法，根据公示L' = k├ ( (L-Lmin)/(Lmax-Lmin)┤ )^r，指数γ确定图像的平均亮度如何相对于其他像素而改变。 对于r= 1，所有像素将被均匀地缩放，而对于r> 1，平均亮度将相对较暗，而对于r<1，它将相对较轻(The exponent r determines how the mean luminance of the image will change relative to other pixels.)
Platform: |
Size: 1024 |
Author: 321手背后
|
Hits:
Description: Rician衰落通道用于对视线（LOS）进行建模，其中因子K被定义为LOS的平均功率与非视线（NLOS）的平均功率的比值。 修改b）中的代码，分别实现K = -40 dB和15 dB的Rician衰落。(A Rician fading channel is used to model the channel with line of sight (LOS), where a factor K is defined as the ratio of the mean power of the LOS to the mean power of the non-line of sight (NLOS). Modify the code in b) to implement Rician fading with K=-40 dB and 15 dB, respectively.)
Platform: |
Size: 1024 |
Author: 呼哧呼哧
|
Hits:
Description: The number of states in GMM as the generative model of the frames is obtained using
k-means algorithm. This also helps to initialize the mean vector and the covariance
matrix of the individual state of the GMM. The training LPC frames collected from
three speech segments are subjected to PCA for dimensionality reduction and are
subjected to k-means algorithm. The total number of frames is equal to the total
number of vectors that are subjected to k-means clustering.
Platform: |
Size: 728064 |
Author: Khan17
|
Hits:
Description: k均值聚类 算法接受参数 k ;然后将事先输入的n个数据对象划分为 k个聚类以便使得所获得的聚类满足:同一聚类中的对象相似度较高;而不同聚类中的对象相似度较小。聚类相似度是利用各聚类中对象的均值所获得一个"中心对象"(引力中心)来进行计算的。(kmeans cluster K means clustering algorithm accepts parameters K; N data object classification and the previously input for K clustering in order to make the cluster available to meet the same object: high similarity clustering; and different clustering objects in lower similarity. The clustering similarity is calculated by using a central object (gravity center) obtained from the mean value of objects in each cluster.)
Platform: |
Size: 1024 |
Author: 看看联合是啥
|
Hits:
Description: K-Means是聚类算法中的一种，其中K表示类别数，Means表示均值。顾名思义K-Means是一种通过均值对数据点进行聚类的算法。K-Means算法通过预先设定的K值及每个类别的初始质心对相似的数据点进行划分。并通过划分后的均值迭代优化获得最优的聚类结果。(K-Means is one of the clustering algorithms, in which K represents the number of classes, and Means means the mean. As the name suggests, K-Means is an algorithm for clustering data points by means of mean. K-Means algorithm divides the similar data points by the preset K value and the initial centroid of each category. The optimal clustering results are obtained by means of the mean iterative optimization after division.)
Platform: |
Size: 273408 |
Author: DragonFZJ
|
Hits:
Description: 设有色噪声序列{e(k)}为
1 1 2
1 1 2 3
( ) ( ) ( ) 1 0.5 0.2 ( )
( ) 1 1.5 0.7 0.1
e k C z k z z k
D z z z z
ξ ξ
? ? ?
? ? ? ?
+ +
= =
? + +
式中，ξ (k)为方差为 1的白噪声。
注意：仿真程序中，白噪声在MATLAB 中由randn 函数产生，randn 函数前
面的系数表示白噪声的均方差，而非方差。(Set colored noise sequence {e (k)}
112
1123
() () () () 10.5 0.2 ()
() 11.5 0.7 0.1
E K C Z K Z Z K
D Z Z Z Z
Zeta Zeta
- - -
- - - -
+ +
= =
- + +
In the formula, zeta (k) for the white noise variance of 1.
Note: in the simulation program, white noise is generated by the randn function in MATLAB, before the randn function
The coefficients of the surface represent the mean square variance of white noise, but not the variance.)
Platform: |
Size: 8192 |
Author: manler
|
Hits:
Description: 我提供了一个基于FXLMS的单通道前馈有源噪声控制系统的一个小例子.
为了减少噪声，我们使用控制器C（z）产生另一个“噪声”yw（k）。我们希望它破坏性地干扰X（k）。这意味着控制器必须是传播介质P（z）的模型。采用最小均方算法对控制器系数/权重进行调整。
然而，还有流体介质S（Z）留在致动器和传感器之间。我们称之为二次传播路径。所以，为了使解正确，我们需要用SH（z）来补偿调整过程，这是S（z）的估计(I provided him a little example of a single channel feed-forward active noise control system based on the FxLMS.
To reduce noise, we generate another 'noise' yw(k) using the controller C(z). We hope that it destructively interferes x(k). It means that the controller has to be a model of the propagation medium P(z). Least mean square algorithm is applied to adjust the controller coefficient/weight.
However, there is also fluid medium S(z) that stay between the actuator and sensor. We called it the secondary propagation path. So, to make the solusion right, we need to compensate the adjustment process using Sh(z), which is an estimate of S(z).)
Platform: |
Size: 2048 |
Author: laoning66 |
Hits:
Description: 文件夹中主要有二维的K-means，gmm，mean-shift，三维的K-means聚类算法的程序，同时已经经过本人验证无误，可以成功运行，有问题的可以私下交流。(Folder mainly two-dimensional k-means, GMM, mean-shift, three-dimensional k-means clustering algorithm procedures, at the same time has been verified by myself, can be successfully run, there are problems can be private communication.)
Platform: |
Size: 10240 |
Author: xuebiao |
Hits:
Description: SURROGATES工具箱是一个多维函数逼近和优化方法的通用MATLAB库。当前版本包括以下功能：
实验设计：中心复合设计，全因子设计，拉丁超立方体设计，D-optimal和maxmin设计。
代理：克里金法，多项式响应面，径向基神经网络和支持向量回归。
错误和交叉验证的分析：留一法和k折交叉验证，以及经典的错误分析（确定系数，标准误差;均方根误差等;）。
基于代理的优化：高效的全局优化（EGO）算法。
其他能力：通过安全裕度进行全局敏感性分析和保守替代。(SURROGATES Toolbox is a general-purpose MATLAB library of multidimensional function approximation and optimization methods. The current version includes the following capabilities:
Design of experiments: central composite design, full factorial design, Latin hypercube design, D-optimal and maxmin designs.
Surrogates: kriging, polynomial response surface, radial basis neural network, and support vector regression.
Analysis of error and cross validation: leave-one-out and k-fold cross-validation, and classical error analysis (coefficient of determination, standard error; root mean square error; and others).
Surrogate-based optimization: efficient global optimization (EGO) algorithm.
Other capabilities: global sensitivity analysis and conservative surrogates via safety margin.)
Platform: |
Size: 362496 |
Author: pluto1888 |
Hits:
«
1
2
...
23
24
25
26
27
28»