-
PAPR_PTS_SLM
有关SLM的调制方式，希望大家好好学习，天天向上，努力提高自己(SLM modulation on, I hope you study hard, every day, strive to improve their)
- 2011-01-20 21:29:01下载
- 积分：1
-
frequency
matlab编程计算结构系统的频率响应程序(matlab program to calculate the frequency response of structural systems)
- 2013-10-11 20:40:23下载
- 积分：1
-
Probabilistic-Graphical-Models
很经典的书籍哦，有1200多页，非常经典的概率图模型书籍，网上价格一般都1000多元哦！(it is very good for you!)
- 2014-11-26 20:42:51下载
- 积分：1
-
MinimosCuadradosL.m
minim squares matlab numerical
- 2012-01-26 20:30:23下载
- 积分：1
-
truetime_code
《网络控制系统分析与设计》-源代码，用matlab的truetime工具箱，与《网络控制系统》-第九章系统仿真分析，一书有一定的关系，可以借鉴，不能完全对应。(" Network Control System Analysis and Design" - the source code, with the truetime matlab toolbox, and the " network control system" - Simulation and Analysis of Chapter IX, a book has a certain relationship, can learn, can not correspond exactly.)
- 2010-10-14 08:51:14下载
- 积分：1
-
06310047
Quantitative Image Recovery From Measured Blind
Backscattered Data Using a Globally
Convergent Inverse Method
- 2013-07-09 10:01:04下载
- 积分：1
-
imageproc
Image processing functions. AMBE, median 2D, image standard deviation of a pdf
- 2013-10-05 05:21:23下载
- 积分：1
-
kmeans
kmeans算法的描述，源代码实现，matlab上 的程序(kmeans)
- 2009-04-30 17:04:09下载
- 积分：1
-
Final_thesis
my final thesis on work of DSK development i included all the source code here.
- 2015-04-11 12:22:01下载
- 积分：1
-
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
