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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
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fecgm
独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE.
Jade performs `Source Separation in the following sense:
X is an n x T data matrix assumed modelled as X = A S + N where
o A is an unknown n x m matrix with full rank.
o S is a m x T data matrix (source signals) with the properties
a) for each t, the components of S(:,t) are statistically
independent
b) for each p, the S(p,:) is the realization of a zero-mean
`source signal .
c) At most one of these processes has a vanishing 4th-order
cumulant.
o N is a n x T matrix. It is a realization of a spatially white
Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance
sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE.
Jade performs `Source Separation in the following sense:
X is an n x T data matrix assumed modelled as X = A S+ N where
o A is an unknown n x m matrix with full rank.
o S is a m x T data matrix (source signals) with the properties
a) for each t, the components of S(:,t) are statistically
independent
b) for each p, the S(p,:) is the realization of a zero-mean
`source signal .
c) At most one of these processes has a vanishing 4th-order
cumulant.
o N is a n x T matrix. It is a realization of a spatially white
Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance
sigma. This is probably better than no modeling at all...)
- 2010-05-27 23:08:51下载
- 积分：1
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Givensr
this source code is very good
- 2013-08-16 01:26:37下载
- 积分：1
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giga
CDMA giga模块 CDMA开发资料 AT指令集(CDMA giga module development data CDMA AT command set)
- 2010-09-07 12:09:11下载
- 积分：1
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Regressionanalysis
回归的各种分析实例以及matlab代码，包括各种数学应用，比如线性回归，非线性回归，逐步回归(Examples of regression analysis, as well as a variety of matlab code, including a variety of mathematical applications such as linear regression, nonlinear regression, stepwise regression)
- 2020-09-18 09:07:56下载
- 积分：1
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12
说明： matlab 转化为索引图像 显示近似匹配后的图像(matlab matlab matlab matlab)
- 2010-04-24 09:05:11下载
- 积分：1
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cliffs.algebra.2--0764563718
Linear Algebra - Math book
- 2010-06-09 09:15:41下载
- 积分：1
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DSB_AM_P1
模拟通信系统中的DSB-AM仿真，利用其原理在理想环境相对其进行的仿真。(Simulation of Communication Systems Simulation DSB-AM, using the principle in the ideal environment relative to the simulation.)
- 2010-11-30 14:28:40下载
- 积分：1
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AHP
层次分析法（AHP），可以用来对于选择学校选择职位之类的事情进行决策(Professor Satty proposed AHP)
- 2009-05-29 20:28:20下载
- 积分：1
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addaxis2
向matlab图中添加坐标，简化坐标添加过程(Matlab figure to add coordinates to simplify the process of adding coordinates)
- 2011-02-15 22:31:32下载
- 积分：1
