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Parzen
- Parzen窗函数概率密度估计演示程序 完全按照《现代模式识别》孙即祥著作 2.4.4《动态聚类法》算法3实现 使用欧式距离作为测度标准。
parzendm
- 模式识别中的parzen窗估计概率密度的一个自编的函数
Parzen_KNN
- Parzen 窗 和 K近邻法进行概率密度估计 还带一个示波器控件.-Parzen window and K-nearest neighbor method probability density is estimated to bring an oscilloscope control.
parzen.rar
- 用parzen窗方法，估计概率密度，采用高期核函数。。。。,With parzen window means of estimating the probability density function using high nucleus. . . .
parzen
- 这是一个模式识别中的parzen窗的一个简单仿真分类实例，其中female.txt和male.txt是训练样本，test.txt是测试样本，分类效果非常好，对于模式学习的初学者将会有很大帮助。-This is a pattern recognition in a simple window parzen Category simulation examples, one of female.txt and male.txt training samples, test.txt is the me
Parzen_Window
- 模式识别中Parzen窗函数的实现，模拟对二维高斯分布的非参估计。窗函数有三种：高斯窗、指数窗、方窗-Pattern Recognition Parzen window function of the realization, simulation of two-dimensional Gaussian distribution is estimated non-participation. There are three window function: Gaussian window, th
parzen
- 这是一个有关parzen窗估计的代码，用来估计概率密度函数，在模式识别中有很多重要的地位-This is a window of the estimated parzen code, used to estimate the probability density function, in the pattern recognition there are many important position ~ ~
Ex1
- 模式识别某次课程的作业，完成了高斯分布下的两种贝叶斯分类器,以及非参数的K近邻、Parzen窗方法，采用UCI机器学习数据库中的某些数据作为样本，使用交叉验证方法确定参数-Pattern recognition of a particular course work, completed under the two Gaussian Bayesian classifier, and the non-parametric K-nearest neighbor, Parzen window meth
parzen
- 这是模式识别的一个大作业，其中包括了parzen窗的原理，算法和源程序，还有对结果的分析。-This is a great job of pattern recognition, including parzen window theory, algorithms and source code, as well as the results of the analysis.
parzenmoshishibie
- 用parzen来计算所选的数据的概率密度函数，所选的窗函数是方窗，最后基于最小错误率的贝叶斯进行分类-With parzen selected data to calculate the probability density function, the selected window function is the side window, and finally the smallest error rate based on Bayesian classification
parzen
- Parzen窗估计法是一种具有坚实理论基础和优秀性能的非参数函数估计方法，它能够较好地描述多维数据的分布状态。-Parzen window estimation method is a non-parametric function estimation method has a solid theoretical basis and excellent performance, it can be used to describe the distribution of state of th
probability-estimation
- 给定若干三维数据，建立训练概率模型，并对新数据进行估计。包括高斯模型、Parzen窗和K近邻密度估计-Given a number of three-dimensional data, the establishment of training probability model, and the new data is estimated. Including the Gaussian model, Parzen windows and K nearest neighbor density e