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RBFNN
rbf神经网络程序,能够进行函数拟合等功能(rbf neural network )
- 2012-05-06 20:58:42下载
- 积分:1
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SVMRFE
用于特征排序 特征提取前的预处理 对于m*n列的数据进行特征排序 模式识别中的特征提取或者模式分类中的预处理 (Sort feature for feature extraction pre-treatment before the column for the m* n order to characterize the data in the application of pattern recognition)
- 2011-10-25 17:23:16下载
- 积分:1
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Under-Anechoic-Blind-e
Underdetermined Anechoic Blind Source
- 2014-02-20 08:43:07下载
- 积分:1
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matlab绘图命令
matlab绘图命令-matlab graphics command
- 2023-06-14 15:55:03下载
- 积分:1
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TOAEstimate
基于TOA(到达时间法)测距的定位算法程序 对学习定位的有用(Location algorithm based on TOA (time of arrival) ranging program of learning positioning useful)
- 2021-03-30 01:59:10下载
- 积分:1
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MATLAB命令行下mcc命令示范3
MATLAB命令行下mcc命令示范3-MATLAB command line mcc orders Model 3
- 2022-11-02 01:45:03下载
- 积分:1
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usingsimulinksimultionsignalsample
使用simulink建模仿真信号的采样与恢复,还可以观察频谱特性(Simulink modeling and simulation of signal sampling and recovery can also be observed spectral characteristics of)
- 2013-01-07 16:26:17下载
- 积分:1
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ID3
该源码使用ID3算法进行分类,该程序为matlab源码。(Classify using the ID3 algorithm)
- 2012-05-01 15:03:24下载
- 积分:1
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ANP
网络分析法(ANP)作为一种决策过程,它提供了一种表示决策因素测度的基本方法。这种方法采用相对标度的形式,并充分利用了人的经验和判断力。在递阶层次结构下,它根据所规定的相对标度—比例标度,依靠决策者的判断,对同一层次有关元素的相对重要性进行两两比较,并按层次从上到下合成方案对于决策目标的测度。(As an decision-making process, Network Analysis (ANP) provides a basic method for measuring the measurement of decision factors.This approach takes the form of a relative scale and takes full advantage of human experience and judgment.Under the hierarchical structure of the hierarchy, it relies on the relative scale of the scale-scale scale, and relies on the judgment of the decision makers to compare the relative importance of the elements of the same level in pairs, and to synthesize the scheme from top to bottom according to the hierarchy.)
- 2021-03-06 18:09:30下载
- 积分:1
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斯坦福machine learning assignment代码(100分版)
应用背景
斯坦福machine learning课程 assignment代码(100分 optional全部完成)
In this exercise, you will implement linear regression and get to see it work
on data.
关键技术Part Submitted File Points
Warm up exercise warmUpExercise.m 10 points
Compute cost for one variable computeCost.m 40 points
Gradient descent for one variable gradientDescent.m 50 points
Total Points 100 points
Optional Exercises
Part Submitted File Points
Feature normalization featureNormalize.m 0 points
Compute cost for multiple
variables
computeCostMulti.m 0 points
Gradient descent for multiple
variables
gradientDescentMulti.m 0 points
Normal Equations normalEqn.m 0 points
- 2022-08-16 16:05:14下载
- 积分:1