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数据矩阵运用非负矩阵分解算法 Cochleagram ISNMF2D

于 2018-10-19 发布文件大小:3532KB

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数据矩阵运用非负矩阵分解算法，分解得到两个低维矩阵，并用于信号分离。(The data matrix is decomposed into two low-dimensional matrices by non-negative matrix decomposition algorithm and used for signal separation.)

文件列表：

Cochleagram ISNMF2D\06317206.pdf, 3637982 , 2014-05-28
Cochleagram ISNMF2D\cochleagram.m, 1173 , 2009-03-25
Cochleagram ISNMF2D\cochplot.m, 1139 , 2013-10-06
Cochleagram ISNMF2D\demo.asv, 1119 , 2014-06-01
Cochleagram ISNMF2D\demo.m, 1116 , 2014-06-01
Cochleagram ISNMF2D\erb2hz.m, 265 , 2009-03-25
Cochleagram ISNMF2D\f_af_bf_cf.mat, 1280 , 2009-03-25
Cochleagram ISNMF2D\fft2gammatonemx.m, 4039 , 2009-03-30
Cochleagram ISNMF2D\gammatoneFast.m, 1604 , 2009-03-25
Cochleagram ISNMF2D\hz2erb.m, 266 , 2009-03-25
Cochleagram ISNMF2D\ibm.m, 1697 , 2009-03-25
Cochleagram ISNMF2D\is_nmf2D_em.m, 3444 , 2014-05-28
Cochleagram ISNMF2D\is_nmf2D_mu.asv, 2979 , 2014-05-28
Cochleagram ISNMF2D\is_nmf2D_mu.m, 2956 , 2014-05-28
Cochleagram ISNMF2D\isp_nmf2d_rec.m, 535 , 2009-08-04
Cochleagram ISNMF2D\isp_nmf2d_rec1.m, 322 , 2012-03-09
Cochleagram ISNMF2D\loudness.m, 1166 , 2009-03-25
Cochleagram ISNMF2D\meddis.m, 1685 , 2009-03-25
Cochleagram ISNMF2D\mfig.m, 520 , 2009-08-25
Cochleagram ISNMF2D\pianotrump.wav, 156084 , 2010-01-02
Cochleagram ISNMF2D\synthesisFast.m, 3271 , 2009-03-25
license.txt, 1307 , 2014-12-02

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PCAcode
实现pca功能的完整matlab程序，很好用,源代码(Pca functional integrity of the implementation of the matlab program, very good, and the source code)

2009-04-08 21:46:56下载

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2007-12-29 19:55:29下载

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2013-09-02 16:21:51下载

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2013-12-20 14:29:14下载

积分：1
a
说明：决策树(Decision Tree）是在已知各种情况发生概率的基础上，通过构成决策树来求取净现值的期望值大于等于零的概率，评价项目风险，判断其可行性的决策分析方法，是直观运用概率分析的一种图解法。由于这种决策分支画成图形很像一棵树的枝干，故称决策树。在机器学习中，决策树是一个预测模型，他代表的是对象属性与对象值之间的一种映射关系。Entropy = 系统的凌乱程度，使用算法ID3, C4.5和C5.0生成树算法使用熵。这一度量是基于信息学理论中熵的概念。决策树是一种树形结构，其中每个内部节点表示一个属性上的测试，每个分支代表一个测试输出，每个叶节点代表一种类别。分类树（决策树）是一种十分常用的分类方法。它是一种监督学习，所谓监督学习就是给定一堆样本，每个样本都有一组属性和一个类别，这些类别是事先确定的，那么通过学习得到一个分类器，这个分类器能够对新出现的对象给出正确的分类。这样的机器学习就被称之为监督学习。(Decision tree is a decision analysis method based on the known probability of occurrence of various situations, which can calculate the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility by constructing a decision tree. It is a graphic method of intuitively using probability analysis. Because this kind of decision branch is drawn as a graph, it is very similar to the branch of a tree, so it is called decision tree. In machine learning, decision tree is a prediction model, which represents a mapping relationship between object attributes and object values. Entropy = the disorder degree of the system, using algorithms ID3, C4.5 and C5.0, spanning tree algorithm using entropy. This measure is based on the concept of entropy in information theory.)

2021-02-01 15:02:09下载

积分：1
signal_analyzer(1)
主要内容(signal_analyzer.rar)如下： test1.txt 第一列为时间t，0.001s为步长；第二列为系统的输入信号；第三列为系统的输出信号。 signal_analyzer.m首先将test1.txt 的数据读入workspace，然后进行FFT，分析出两信号的频率，输出对于输入的幅值衰减（db），输出对输入的相位差。 (Main Content (signal_analyzer.rar) as follows: test1.txt the first time as t, 0.001s for the step the second as the input signal the third as the system output signal. signal_analyzer.m first test1.txt the data read into the workspace, and then FFT, the frequency of the two signals, the output amplitude for input attenuation (db), the output of the input phase difference.)

2010-05-18 15:57:01下载

积分：1

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