资源列表
hanniu_v31
- 可实现对二维数据的聚类，采用了小波去噪的思想，一种基于多文档得图像合并技术。- Can realize the two-dimensional data clustering, Using wavelet denoising thought, Based on multi-document image obtained combining technique.
henjao_v63
- 本程序的性能已经超过其他算法，ofdm系统仿真 含16qam调制 fft 加窗 加cp等模块，是信号处理的基础。- This program has exceeded the performance of other algorithms, ofdm system simulation including 16qam modulation fft windowing modules plus cp, Is the basis of the signal processing.
hiegan
- 能量熵的计算，Matlab实现界面友好，对于初学matlab的同学会有帮助。- Energy entropy calculation, Matlab to achieve user-friendly, Matlab for beginner students will help.
hingfie_v73
- 时间序列数据分析中的梅林变换工具，可以广泛的应用于数据预测及数据分析,包含位置式PID算法、积分分离式PID。- Time series data analysis Mellin transform tool, Can be widely used in data analysis and forecast data, It contains positional PID algorithm, integral separate PID.
matlab金融代码
- matlab统计分析与应用的40个案例分析中的部分代码！
xgboost
- xgboost算法源码，含基本示例（训练、建模、预测）。-Xgboost algorithm source code, including the basic examples (training, modeling, forecasting).
machine-learning-ex1
- 斯坦福大学机器学习课程作业第一章，主要包含线性回归实战。-Stanford Machine Learning course work first chapter contains a linear regression combat.
machine-learning-ex2
- 斯坦福大学机器学习课程作业第二章，主要包含逻辑逻辑回归算法实战。-Stanford University machine learning course of the second chapter, including logic logic regression algorithm combat.
machine-learning-ex3
- 斯坦福大学机器学习课程作业第三章，主要包含人工神经网络算法初阶实战。-Stanford University machine learning course work The third chapter, mainly contains the artificial neural network algorithm initial combat.
machine-learning-ex4
- 斯坦福大学机器学习课程作业第四章，主要包含人工神经网络算法进阶实战。-Stanford University machine learning course of the fourth chapter, mainly including artificial neural network algorithm advanced combat.
machine-learning-ex5
- 斯坦福大学机器学习课程作业第五章，主要包含加正则项的线性回归算法实战。-Stanford University machine learning course of the fifth chapter, including Regularized Linear Regression algorithm combat.
hingmen
- 算法优化非常好，几乎没有循环，毕业设计有用，具有丰富的参数选项。- Algorithm optimization is very good, almost no circulation, Graduation useful It has a wealth of parameter options.