搜索资源列表
ClassicalELM
- 通过极限学习机的相关算法，实现数据的预测、回归、分类，从而有利益数据的处理-Processed through the relevant algorithm ELM achieve prediction data, regression, classification, and thus interest data
pythonsrc
- 机器学习算法，包括主成分分析方法，奇异值分解，逻辑回归，最小二乘法线性回归，朴素贝叶斯-machine learning algorithm prototype including PCA, SVD, Logic Regression, LMS and Naive Bayes
svm
- 支持向量机python实现算法，回归、分类、预测-surpport vector machion
Locally-Line-Regress
- 《斯坦福公开课》局部线性回归算法（PS1）。-Locally Line Regress
arima
- ARMA,AR,MA,ARIMA等实现自回归预测、齐次稳定回归预测算法-ARMA, AR, MA, ARIMA, etc. to achieve autoregressive prediction, homogeneous and stable regression prediction algorithm
logRegres
- 机器学习中的逻辑回归算法，利用python实现的算法-Logic regression algorithm in machine learning, using Python to achieve the algorithm
LogitTwice
- 基于逻辑回归的二分类算法代码，能很好的实现-Binary logistic regression based algorithm code, can achieve a good
svm_python
- 在机器学习领域，支持向量机SVM(Support Vector Machine)是一个有监督的学习模型，通常用来进行模式识别、分类、以及回归分析。本程序是SVM的python实现，用的是SMO算法。只能进行分类，并且能够显示图形结果。-In the field of machine learning, support vector machines SVM (Support Vector Machine) is a supervised learning model is usually use
Python-ELM-master
- 经典的ELM算法，适合做小样本回归预测，亲测有用，亲测有效-The classical ELM algorithm, suitable for small sample regression, pro test useful, pro test effective
logRegres
- Logistic回归主要目的是寻找一个非线性sigmoid的最佳拟合参数，采用梯度上升算法可以达到局部的最优化-The main purpose of Logistic regression is to find the best fitting parameters of a nonlinear sigmoid, and the gradient ascent algorithm can be used to achieve the local optimization.
SVM_GUI_3.1[mcode]
- faruto编写的基于libsvm3.1的SVM_GUI，可用于SVM分类及相关回归分析，已经集成了GA及PSO参数寻优算法及PCA算法，提供的是GUI版本及与之对应的源码版本-SVM_GUI and the program of SVM_Code,base on the version of the Libsvm 3.1，using the GA and PSO algorithm to improve
Wind-Speed-Combined-Prediction
- 针对风电场短期风速的预测提出一种基于小波变换的组合预测方法。首先利用Mallat 算法对短期风速时间序列进行db3 小波三层分解与重构，得到短期风速时间序列的近似分量和细节分量。针对近似分量和细节分量的不同特性，对近似分量利用粒子群算法优化的最小二乘支持向量机进行预测，对细节分量利用自回归求和滑动平均模型进行预测。最后各预测模型预测值组合叠加得到最终的短期风速预测值。仿真结果表明该方法具有较高的预测准确度。-In order to improve short-term wind speed pr
SILPMKL
- 用于回归的多核学习之MKL-wrapper算法程序，编写语言为python，运行环境为linux。-For multi-core the MKL- wrapper of learning algorithm of regression procedure, is the written language python, running environment for Linux.
lwpr
- LWPR 局部加权投影回归算法，是一种高效的数据降维方法，也能用于预测方法研究。 -LWPR is an effective dimensionality reduction approach. It can be used for prediction.
project
- 数据挖掘，推荐系统，堆叠降噪自编码器，逻辑回归(Data mining, recommender systems, stack noise reduction, self coder, logic regression)
python-logistic
- 对因变量为0-1属性变量，利用Logistic算法，对其进行回归预测(The dependent variable is the 0-1 attribute variable, and the Logistic algorithm is used to predict it)
rlhust
- 可以进行曲线回归拟合算法的四参数算法,函数为 y (a-d) (1+(x c) b) +d ec50 m 为其主要函数()
ga-svm
- 用遗传算法优化支持向量回归机C、g、p参数(Optimization of C, G, P parameters of support vector regression machine by genetic algorithm)
ELM
- 一种神经网络算法：极限学习机（ELM)，包括分类和回归，仿真验证无误，适合初学者练习(A data mining algorithm: limit learning machine (ELM), including classification and regression, simulation verification is unmistakable, suitable for beginners to practice)
11560948
- 数据挖掘中的重要算法：自回归滑动平均时间序列算法,用于时序数据挖掘()