-
weina
维纳滤波器程序主要适用于dsp系统下的运作和开发！(weina filter program
)
- 2013-12-20 21:02:08下载
- 积分：1
-
PSOMADS0
该算法是粒子群算法和网格直接搜索算法的结合，具有稳定性高的优点。(This is a combination of particle swarm optimization algorithm and grid direct search algorithm.)
- 2021-04-07 16:29:01下载
- 积分：1
-
book_total
Intuitive Probability and Random Processes using MATLAB by Steven M. Kay
- 2009-09-29 02:47:27下载
- 积分：1
-
ANN_Classifier
说明： matlab应用实例：基于神经网络的分类，用的是iris数据集做例子(matlab applications: classification based on neural network, using the iris data set an example)
- 2010-05-03 17:24:10下载
- 积分：1
-
timefeature
features in time domaim for signal processing
- 2010-07-24 19:26:13下载
- 积分：1
-
power
潮流计算的好程序，c++，潮流计算的好程序，c++(Good program flow calculation, c++)
- 2014-02-21 00:13:07下载
- 积分：1
-
saliency
一篇经典论文的代码，“Saliency,Scale,image description”，用于寻找视觉显著性的区域，matlab代码(A classic papers of the code, " Saliency, Scale, image description" , for the visual search of the area significantly, matlab code)
- 2009-06-17 23:39:12下载
- 积分：1
-
CCI
Analysis of outage probability in mobile cellular system due to co channel interference.
- 2011-01-23 13:06:33下载
- 积分：1
-
cluster
二维多密度网格聚类算法，主要是完成二维数据的网格聚类，采用的是多密度临近的算法。(Two-dimensional density grid clustering algorithm)
- 2012-05-25 15:57:12下载
- 积分：1
-
adaboost
AdaBoost元算法属于boosting系统融合方法中最流行的一种，说白了就是一种串行训练并且最后加权累加的系统融合方法。
具体的流程是：每一个训练样例都赋予相同的权重，并且权重满足归一化，经过第一个分类器分类之后，
计算第一个分类器的权重alpha值，并且更新每一个训练样例的权重，然后再进行第二个分类器的训练，相同的方法.......
直到错误率为0或者达到指定的训练轮数，其中最后预测的标签计算是各系统*alpha的加权和，然后sign（预测值）。
可以看出，训练流程是串行的，并且训练样例的权重是一直在变化的，分错的样本的权重不断加大，正确的样本的权重不断减小。
AdaBoost元算法是boosting中流行的一种，还有其他的系统融合的方法，比如bagging方法以及随机森林。
对于非均衡样本的处理，一般可以通过欠抽样(undersampling)或者过抽样(oversampling)，欠抽样是削减样本的数目，
过抽样是重复的选取某些样本，最好的方法是两种进行结合的方法。
同时可以通过删除离决策边界比较远的样例。
(AdaBoost boosting systems dollar fusion algorithm is the most popular one, it plainly systems integration approach is a serial train and final weighted cumulative.
Specific process is: Each training example is given equal weight, and the weights satisfy normalization, after the first classifiers after
Calculating a first classifier weights alpha value for each sample and updates right weight training, and then the second classifier training, the same way .......
0, or until the specified error rate training rounds, wherein the label is the calculation of the final prediction system* alpha weighted and then sign (predicted value).
As can be seen, the training process is serial, and weight training examples is always changing, the right of the wrong sample weight continued to increase, the right to correct sample weight decreasing.
AdaBoost algorithm is an element, as well as other methods of boosting popular systems integration, such as bagging and random forest method.
For )
- 2014-07-09 19:24:29下载
- 积分：1
