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chapter13
说明： 《数字图像处理与机器视觉:Visual C++与Matlab实现》6
支持向量机,综合案例——基于PCA和SVM的人脸识别系统(" Digital image processing and machine vision: Visual C++ and Matlab to achieve" 6 support vector machines, comprehensive case- based on PCA and SVM for Face Recognition System)
- 2010-04-22 09:20:07下载
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802.16channel-design-and-realize
國 立 清 華 大 學 碩 士 論 文 符合802.16 標準之通道模擬器之研製(Chapter 1 Introduction
Chapter 2 Introduction of microscope effects
Chapter 3 Introduction of macroscope and system effects .21
Chapter 4 Channel model simulation
Chapter 5 Works before implementing the hardware
Chapter 6 Hardware implementation
Chapter 7 Conclusions and future directions
)
- 2014-11-05 14:40:26下载
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leadlag
Compensadores en adelanto y atraso
- 2012-11-29 04:23:59下载
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mamdani_ipmt
fuzzy controller design for inverted pendulum
- 2014-01-25 15:54:50下载
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EasyKrig_V3
克里金法广泛地应用于地下水模拟、土壤制图等领域，是一种很有用的地质统计格网化方法。它首先考虑的是空间属性在空间位置上的变异分布．确定对一个待插点值有影响的距离范围，然后用此范围内的采样点来估计待插点的属性值。该方法在数学上可对所研究的对象提供一种最佳线性无偏估计(某点处的确定值)的方法。它是考虑了信息样品的形状、大小及与待估计块段相互间的空间位置等几何特征以及品位的空间结构之后，为达到线性、无偏和最小估计方差的估计，而对每一个样品赋与一定的系数，最后 进行加权平均来估计块段品位的方法。(Kriging is a technique that provides the Best Linear Unbiased Estimator of the unknown fields (Journel and Huijbregts, 1978 Kitanidis, 1997). It is a local estimator that can provide the interpolation and extrapolation of the originally sparsely sampled data that are assumed to be reasonably characterized by the Intrinsic Statistical Model (ISM). An ISM does not require the quantity of interest to be stationary, i.e. its mean and standard deviation are independent of position, but rather that its covariance function depends on the separation of two data points)
- 2013-11-27 09:56:06下载
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MATLAbebook
matlab的一些经典书籍。基础与应用，综合辅导，小波分析(matlab some classic books. Basic and applied, integrated counseling, wavelet analysis)
- 2008-07-10 14:47:55下载
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matlab
有关matlab 各种命令的一些集合，可以来看看(a set of matlab)
- 2012-11-26 17:13:55下载
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lssvm
lssvm All plotting is done with this simple command. It looks for the best way of displaying the result
- 2017-07-06 11:19:56下载
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ex33f
another trapezoidal profile for matlab
- 2010-11-19 16:26:11下载
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MyKmeans
实现聚类K均值算法： K均值算法：给定类的个数K，将n个对象分到K个类中去，使得类内对象之间的相似性最大，而类之间的相似性最小。 缺点：产生类的大小相差不会很大，对于脏数据很敏感。 改进的算法：k—medoids 方法。这儿选取一个对象叫做mediod来代替上面的中心 的作用，这样的一个medoid就标识了这个类。步骤： 1，任意选取K个对象作为medoids（O1,O2,…Oi…Ok）。 以下是循环的： 2，将余下的对象分到各个类中去（根据与medoid最相近的原则）； 3，对于每个类（Oi）中，顺序选取一个Or，计算用Or代替Oi后的消耗—E（Or）。选择E最小的那个Or来代替Oi。这样K个medoids就改变了，下面就再转到2。 4，这样循环直到K个medoids固定下来。 这种算法对于脏数据和异常数据不敏感，但计算量显然要比K均值要大，一般只适合小数据量。(achieving K-mean clustering algorithms : K-means algorithm : given the number of Class K, n will be assigned to target K to 000 category, making target category of the similarity between the largest category of the similarity between the smallest. Disadvantages : class size have no great difference for dirty data is very sensitive. Improved algorithms : k-medoids methods. Here a selection of objects called mediod to replace the center of the above, the logo on a medoid this category. Steps : 1, arbitrary selection of objects as K medoids (O1, O2, Ok ... ... Oi). Following is a cycle : 2, the remaining targets assigned to each category (in accordance with the closest medoid principle); 3, for each category (Oi), the order of selection of a Or, calculated Oi Or replace the consumption-E (Or))
- 2005-07-26 01:32:58下载
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