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ssssssss
显得无聊自己做的一段代码显得无聊自己做的一段代码(ssssssssssssssssssssssssss)
- 2009-12-04 16:05:24下载
- 积分：1
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RFmodelAM
amplitude modulation
- 2012-08-07 01:29:02下载
- 积分：1
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FROG
Frequency-resolved optical gating
- 2014-02-17 18:59:09下载
- 积分：1
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数学实验
用MATLAB实现的RSA。界面友好，可加密任意长度字符串。(MATLAB RSA. Friendly interface, encryption arbitrary length of the string.)
- 2005-06-27 17:58:49下载
- 积分：1
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FastICA_2.5
fastica算法，用于盲源分离的一个算法，希望能对大家有用~~~(fastica algorithm for blind source separation of an algorithm, hoping it would be useful ~ ~ ~)
- 2011-05-17 21:11:01下载
- 积分：1
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在TDOA定位系统中，运用平滑的的IMM算法对机动目标进行被动跟踪 ITDOOA Smoothn
在TDOA定位系统中，运用平滑的的IMM算法对机动目标进行被动跟踪
(TDOA positioning system, the use of smooth IMM algorithm for passive tracking of maneuvering targets)
- 2012-09-15 22:26:17下载
- 积分：1
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chapter
matlab遗传算法源代码来对函数优化进行运算(matlab genetic algorithm source code for function optimization computing)
- 2013-04-14 20:38:17下载
- 积分：1
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NearestNeighbour
NEAREST NEIGHBOUR FOR TRAVELLING SALESMAN PROBLEM
- 2013-04-29 17:39:24下载
- 积分：1
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Program2
Differential equations (in this case is the ordinary differential equation) means containing one, two, and even high-differential equations unknowns. Is to solve the partial differential equations, mathematical equations based. There are many special functions, are not drawn to a number of functions or functions beyond the type of analytical solution of differential equations which define them. These special functions are important because they describe the nature of certain phenomena, for example, electronic activities, drum skin vibrations, pendulum swing, and so on.
- 2012-01-04 12:04:06下载
- 积分：1
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fs_sup_relieff
Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重：
如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules:
If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)
- 2018-04-17 14:41:55下载
- 积分：1
