资源列表
Regression
- 一个简单机器学习的范例,内涵基本数据，初学者必备(Examples of simple machine learning)
fiubenfeng
- ofdm system simulation including 16qam modulation fft windowing modules plus cp, Using high-order cumulants of MPSK signal modulation recognition, NRZ type differential phase modulation signal modeling and simulation analysis.
DFIG2015826
- 机侧扩张状态观测器pi控制 前段时间做的感觉还可以新手请多指教(Machine side expansion, state observer, PI control)
test1
- BP神经网络的测试小程序，适合初学者直接使用(BP neural network testing procedures)
Machine Learning in Action
- 机器学习重点推荐点推荐，学习必杀技点推荐，学习必杀技(machine leaing a probabilistic perspective)
gaojao_v84
- Through repeated training CYPJiyFlate have higher recognition rate, Minimum mean square error MSE calculation algorithm, The Chinese have a comment, understand it.
python
- 实现libsvm的python代码实现,这是最新的python实现的开源代码，可以结合anaconda使用(can achieve libsvm's function)
bpanniris
- 这段代码为BP神经网络的学习算法代码，这段代码可以帮助初学者很好的学习bp神经网络知识(This code for the BP neural network learning algorithm code, this code can help beginners learn BP neural network knowledge)
chapter14
- 粒子群算法实现pid寻优,寻找最好的比例。积分，微分参数(liziqun suanfa xun you)
rmebr
- Relief computing classification weight, For beginners with a reference value, It contains CV, CA, Single, current, constant turn rate, turning model.
2047
- Calculation multifractal detrended fluctuation analysis matlab program, Machine learning routines, Rapid expansion of random spanning tree algorithm.
带权重条件熵的属性约简算法
- 粗糙集理论中最重要的内容之一就是属性约简问题，现有的许多属性约简算法往往是基于属性对分类的重要性，如果属性约简的结果能满足用户实际需要的信息，如成本、用户的偏好等，那么约简理论将会有更高的实用价值。基于此，从信息熵的角度定义了带权重的属性重要性，然后重新定义了基于带权重的属性重要性的熵约简算法。最后通过实际例子说明，与基于属性重要性的熵约简算法相比，考虑权重的算法更加符合用户的实际需求。(Attribute reduction is one of the most important conte