分級聚類算法：包括k-mean max-dist min-dist 程序使用方法： 程序中打開文件“.dat”-》選擇聚類方法-》顯示數據 .dat文件格式： 分成幾類 輸入樣本維數 樣本個數 下面依次為樣本特征向量
標簽： dat max-dist min-dist k-mean
上傳時間： 2013-12-22
上傳用戶：alan-ee
this directory contains the following: * The acdc algorithm for finding the approximate general (non-orthogonal) joint diagonalizer (in the direct Least Squares sense) of a set of Hermitian matrices. [acdc.m] * The acdc algorithm for finding the same for a set of Symmetric matrices. [acdc_sym.m](note that for real-valued matrices the Hermitian and Symmetric cases are similar however, in such cases the Hermitian version [acdc.m], rather than the Symmetric version[acdc_sym] is preferable. * A function that finds an initial guess for acdc by applying hard-whitening followed by Cardoso s orthogonal joint diagonalizer. Note that acdc may also be called without an initial guess, in which case the initial guess is set by default to the identity matrix. The m-file includes the joint_diag function (by Cardoso) for performing the orthogonal part. [init4acdc.m]
標簽： approximate directory algorithm the
上傳時間： 2014-01-17
上傳用戶：hanli8870
The toolbox solves a variety of approximate modeling problems for linear static models. The model can be parameterized in kernel, image, or input/output form and the approximation criterion, called misfit, is a weighted norm between the given data and data that is consistent with the model. There are three main classes of functions in the toolbox: transformation functions, misfit computation functions, and approximation functions. The approximation functions derive an approximate model from data, the misfit computation functions are used for validation and comparison of models, and the transformation functions are used for deriving one model representation from another. KEYWORDS: Total least squares, generalized total least squares, software implementation.
標簽： approximate The modeling problems
上傳時間： 2013-12-20
上傳用戶：15071087253
一種c語言算法 k-mean.c
上傳時間： 2013-12-06
上傳用戶：頂得柱
K-MEAN:經典K均值算法,適用領域：語音識別，圖像識別
上傳時間： 2016-10-19
上傳用戶：evil
ClustanGraphics聚類分析工具。提供了11種聚類算法。 Single Linkage (or Minimum Method, Nearest Neighbor) Complete Linkage (or Maximum Method, Furthest Neighbor) Average Linkage (UPGMA) Weighted Average Linkage (WPGMA) Mean Proximity Centroid (UPGMC) Median (WPGMC) Increase in Sum of Squares (Ward s Method) Sum of Squares Flexible (ß space distortion parameter) Density (or k-linkage, density-seeking mode analysis)
標簽： ClustanGraphics Complete Neighbor Linkage
上傳時間： 2014-01-02
上傳用戶：003030
K-mean算法，并通過了IRIS數據的測試。
上傳時間： 2013-12-18
上傳用戶：shawvi
There a t least five Request for Enhancement s (RFE) in the JavaSoft bug database related to Mouse Wheel support in Java. One of the RFE s BugID #4202656 has 281 votes from developers requesting Sun for a fix. Sun has finally agreed to support this feature in JDK 1.4 codenamed Merlin accroding to the BugID #4289845 in its bug database.
標簽： Enhancement JavaSoft database Request
上傳時間： 2016-11-07
上傳用戶：
The inverse of the gradient function. I ve provided versions that work on 1-d vectors, or 2-d or 3-d arrays. In the 1-d case I offer 5 different methods, from cumtrapz, and an integrated cubic spline, plus several finite difference methods. In higher dimensions, only a finite difference/linear algebra solution is provided, but it is fully vectorized and fully sparse in its approach. In 2-d and 3-d, if the gradients are inconsistent, then a least squares solution is generated
標簽： gradient function provided versions
上傳時間： 2016-11-07
上傳用戶：秦莞爾w
PRINCIPLE: Removal of the row mean from each row, followed by division of the row by the respective row standard deviation.
標簽： the row respective PRINCIPLE
上傳時間： 2016-11-27
上傳用戶：z1191176801
