搜索资源列表
PieProgressCtrl_src
- The Code Project is an exciting site with impressive teaching and professional exchange capabilities. I have learned much from it and also decided to submit my contribution. Occasionally I was involved in a project to check experimental data for cons
sbgcop_0.95.tar
- sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric i
sbgcop_0.95
- sbgcop: Semiparametric Bayesian Gaussian copula estimation This package estimates parameters of a Gaussian copula, treating the univariate marginal distributions as nuisance parameters as described in Hoff(2007). It also provides a semiparametric i
Senfore_DragDrop_v4.1
- Drag and Drop Component Suite Version 4.1 Field test 5, released 16-dec-2001 ?1997-2001 Angus Johnson & Anders Melander http://www.melander.dk/delphi/dragdrop/ ------------------------------------------- Table of Contents: ----------------------
IGDIPlus
- IGDIPlus v.3.0 一个Delphi的图像绘画接口, 提供强大的绘画机制.也可以用于C++ Builder-Interface based Delphi friendly GDI+ implementation. Features: * All GDI+ objects are accessed trough interfaces, with automatic release of resources. * All data types are e
dineof.tar
- DINEOF重构数据矩阵缺失点的值-Reconstruction of missing data matrix DINEOF point value ~ ~ ~
Solas32_Setup
- solas3.2，最好的数据缺失填补软件，核心部分是多重填补。目前售价是1290欧元，这个是试用30天版本，可以使用所有功能。-solas3.2, the best data to fill the missing software, the core of the multiple fill. The current price is
ive_sas_windows
- 缺失数据的利器，SAS插件ive，和solas各有所长。-Missing data tool, SAS plug-ive, and SOLAS have their own strong points.
ive_sas_examples
- SAS中的缺失数据填补插件iveware使用的examples。 -SAS to fill in missing data iveware use plug-examples.
BitArrayApp
- The class CBitArray behaves like a specialization of the MFC template class CArray. A real specialization is not possible because of a missing simple data type for a bit. For maximum efficiency the bits are packed into a dynamic array of unsign
K-meansNB
- ：将K—means算法引入到朴素贝叶斯分类研究中，提出一种基于K—means的朴素贝叶斯分类算法。首先用K— me．arks算法对原始数据集中的完整数据子集进行聚类，计算缺失数据子集中的每条记录与 个簇重心之间的相似度，把记 录赋给距离最近的一个簇，并用该簇相应的属性均值来填充记录的缺失值，然后用朴素贝叶斯分类算法对处理后的数据 集进行分类。实验结果表明，与朴素贝叶斯相比，基于K—means思想的朴素贝叶斯算法具有较高的分类准确率。-: K-means algorithm will
AutomaticSpectra
- This toolbox is Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands, Vector Autoregressive modeling and Detection. It requires ARMASA toolbox. This toolbox can be downloaded from the Matlab Central file exch
ASA
- 自动谱分析：可用于丢失/采样/子束光谱分析；矢量自动迭代，可用于建模，故障诊断；-The applications of this additional toolbox are: - Automatic spectral analysis for Irregular sampling/Missing data, analysis of spectral subbands, - Vector Autoregressive modeling and Detection [uses ARMA
interpolation
- 当实行降采样后，某些图像点的数据会丢失。该算法可以通过一些插值算法得到所需的数据。-While interpolation functions are available builtin in MATLAB, it require data in specified griddata format. Consider sampling data from an experiment and you are missing some points and you don t want to rep
Tutorial-Algorithmsfor2-DObjectRecognition
- Tutorial: Algorithms for 2-Dimensional Object Recognition. Representation of arbitrary shape for purposes of visual recognition is an unsolved problem. The task of representation is intimately constrained by the recognition process and one cannot b
SVDU
- 一种奇异值分解的更新算法，可以用来增量地进行主成分分析，根据文献《Incremental singular value decomposition of uncertain data with missing values》-《Incremental singular value decomposition of uncertain data with missing values》
ARMAsel_mis_irreg
- Autoregressive model for missing data
phase.2.1.1.source.tar
- 预测单倍型根据基因型数据,根据已经知道的数据,预测未知的数据- The program PHASE implements methods for estimating haplotypes from population genotype data described in Stephens, M., and Donnelly, P. (2003). A comparison of Bayesian methods for haplotype reconstruction from p
add-missing-data
- 这是一个Matlab程序，用牛顿插值法，补充缺失的数据，从而保证了数据的完整性。-This is a Matlab program, using Newton s interpolation method, add missing data, thus ensuring data integrity.
处理缺失数据的高级方法
- 数据探索分析中处理缺失数据的高级处理方法(Advanced processing methods for missing data processing in data discovery analysis)