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salveaza_coef
numbers 1.15 in hexa
- 2010-01-22 03:05:45下载
- 积分：1
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DS
说明： DS算法程序和一些例子，有兴趣的可以下载下来验证一下(DS algorithm procedure and some examples are interested can download to test)
- 2010-04-21 18:37:19下载
- 积分：1
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wavelet-design-codes
这是一本很权威的由张德峰老师编写的小波分析的书相应的源代码(This is a very authoritative and written by Zhang Defeng teacher' s book of wavelet analysis the source code)
- 2011-04-24 11:47:08下载
- 积分：1
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shorthand_manual_matlab_function
matlab函数速记手册shorthand manual matlab function(matlab function shorthand manual shorthand manual matlab function)
- 2010-08-02 11:35:06下载
- 积分：1
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wavelet-denoise
利用小波变换进行图像去噪的程序 基于MATLAB的(Image denoising using wavelet transform procedures for MATLAB-based)
- 2010-08-17 16:39:05下载
- 积分：1
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REFLCOEF2
The Seismic Unix Project is partially supported by the CWP Consortium Project. In the past, the Seismic Unix Project has received partial support from the Gas Research Institute (GRI) and the Society of Exploration Geophysicists Foundation.
- 2011-11-11 09:22:48下载
- 积分：1
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spacklenoise
Impulse noise explanation....
- 2011-01-05 12:28:21下载
- 积分：1
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new-npcsam
说明： 改进防碰撞扩展ALOHA的matlab仿真算法(ALOHA anti-collision expansion of the improved simulation algorithm matlab)
- 2011-03-20 11:42:03下载
- 积分：1
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linear_focus_beamform_fft
聚焦波束形成 线阵波束形成算法实现：包括非FFT算法和采用FFT算法的程序(Focused beamforming line array beamforming algorithm: including non-FFT algorithm and uses FFT algorithm procedures)
- 2020-09-08 19:28:04下载
- 积分：1
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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
- 积分：1
