搜索资源列表
OutDDD
- 基于四个特征值的聚类方法 K-均值算法，对于多组数据分成特征相近的两类-Eigenvalues based on four clustering method K-means algorithm, for multiple sets of data are divided into two categories of similar characteristics
training_net
- 双层BP神经网络用于模式识别，4个特征值输入，输出识别结果采用二进制编码-Double-layer BP neural networks for pattern recognition, the four eigenvalues input, output recognition results using binary coding
Clustering
- In multivariate statistics and the clustering of data, spectral clustering[1] techniques make use of the spectrum (eigenvalues) of the similarity matrix of the data to perform dimensionality reduction before clustering in fewer dimensions. The simila
PACKET
- 应用小波包技术提取数据的特征值，该方法十分有效-Wavelet packet data technology to extract eigenvalues, this method is very effective
Numerical_Computing_with_MATLAB_2004
- 非常经典的160个matlab源程序，并且包含说明书（英文版），内容涵盖线性方程组、插值、零和根、最小二乘、正交、常微分方程、傅立叶分析、随机数、特征值和奇异值和偏微分方程-Very classic 160 matlab source, and contains instructions (in English), covering linear equations, interpolation, zero and roots, least squares, quadrature, ordina
ELM分类器
- ELM是基于深度学习的分类器，运算速度快。 在B_data.m里导入待分类矩阵B.mat（1-n列为特征值，n列为标签）；运行B_data.m；再打开fuzzyEn_main.m并运行即可。(ELM is based on depth learning classifier, computing speed. In B_data.m imported matrix to be classified B.mat (1-n as eigenvalues, n as a label); Run B