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BurnCD
用C++做的文件刻录（来自于网上转载），希望大家可以用得上。(File Burning)
- 2010-07-15 08:36:20下载
- 积分：1
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1145455565666
matlab file to calculate trajectory for a given missile geometry and areodynamic very useful for preliminary design
- 2011-01-28 18:00:21下载
- 积分：1
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chengxu
说明： matlab图像分割的实例，包括基于L*a*b空间的彩色图像分割，检测汽车目标，分水岭分割等程序源代码(matlab examples of segmentation, including those based on L* a* b color space, image segmentation, target detection vehicles, watershed segmentation source code)
- 2010-05-05 11:24:41下载
- 积分：1
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VFIT3
矢量匹配算法，关于曲线理和的问题，能克服有理匹配的相关缺陷(vectfiting )
- 2021-03-14 11:59:23下载
- 积分：1
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remainenergy
computes the shortening-signal-to-noise ratio in dB,tail energy in dB
and the effective channel impulse response(computes the shortening- signal-to-nois e ratio in dB. tail energy in dB and the effective channel impu lse response)
- 2007-06-07 19:11:25下载
- 积分：1
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NCUT
此程序是谱聚类中，经典的Ncut算法。用matlab编写的。(This program is spectrum clustering, classic Ncut algorithm.The program
uses matlab。)
- 2012-03-05 21:28:01下载
- 积分：1
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LMS_RLS_sim
功能描述：测试LMS与RLS算法，比较两种算法的收敛特性
文件名：LMS_RLS_sim.m
测试用例：
x(n)+a1*x(n-1)+a2*x(n-2)=e(n)，a1=-1.6,a2=0.81,e(n)为高斯白噪声
文件输出：系数a1的值
调用函数：function [A] = LMS_Algo(M,N,mu,xn)
被调用：无
作者：mingcheng
编写时间：2009-10-13
修改时间：2009-10-13
版本：V1.0 ( Function Description: Test LMS and RLS algorithm, the convergence characteristics were compared file name: LMS_RLS_sim.m test case: x (n)+ a1* x (n-1)+ a2* x (n-2) = e (n), a1 =- 1.6, a2 = 0.81, e (n) is Gaussian white noise file output: the value of coefficient a1 call the function: function [A] = LMS_Algo (M, N, mu, xn) is called: No of: mingcheng write time :2009-10-13 modified :2009-10-13 version: V1.0 )
- 2010-07-11 12:15:54下载
- 积分：1
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suijishu-generator
基于matlab的随机数的生成，包括其他调用的函数，(Random number generation based on MATLAB, including other call function
)
- 2013-08-15 18:37:30下载
- 积分：1
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shenjingwangluo
可自由选择贝叶斯正则化算法或者是L-M 优化算法的神经网络matlab代码(The freedom to choose the Bayesian regularization algorithm or LM optimization algorithm neural network matlab code)
- 2014-09-11 13:29:43下载
- 积分：1
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半监督分类算法
说明： 半监督学习(Semi-Supervised Learning，SSL)是模式识别和机器学习领域研究的重点问题，是监督学习与无监督学习相结合的一种学习方法。半监督学习使用大量的未标记数据，以及同时使用标记数据，来进行模式识别工作。当使用半监督学习时，将会要求尽量少的人员来从事工作，同时，又能够带来比较高的准确性，因此，半监督学习目前正越来越受到人们的重视。(Semi-Supervised Learning (SSL) is a key issue in the field of pattern recognition and machine learning. It is a learning method combining supervised learning with unsupervised learning. Semi-supervised learning uses a large number of unlabeled data, as well as labeled data, for pattern recognition. When using semi-supervised learning, it will require as few people as possible to work, and at the same time, it can bring relatively high accuracy. Therefore, semi-supervised learning is receiving more and more attention.)
- 2021-04-12 11:28:57下载
- 积分：1
