搜索资源列表
ml-py
- 机器学习算法(kNN、逻辑回归、线性回归、朴素贝叶斯)python实现。-machine learning by python
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
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
logistic-regression
- 用matlab实现机器学习算法中的逻辑回归-Using matlab to achieve a machine learning algorithm in a logistic regression
logRegres---python
- 机器学习中的逻辑回归算法，经过测试，可以使用-Logistic regression algorithm of machine learning, through the test, you can use
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.
knn_logistic_naiveBayes
- 统计机器学习经典分类算法MATLAB代码，付数据集。包括knn算法，逻辑斯蒂回归和朴素贝叶斯算法。-Classical statistical machine learning classification algorithm MATLAB code, pay dataset. Including knn algorithm, logistic regression and naive Bayes algorithm.
SparseLR
- 使用Hadoop平台的Spark组件，实现机器学习分类算法LR（逻辑回归），使用的编程语言为Scala。(Using the Spark component of the Hadoop platform, the machine learning classification algorithm LR (logical regression) is used, and the programming language is Scala.
logistic_3
- 机器学习算法，逻辑回归，将数据集划分为两类，优化算法采用随机梯度上升(logistics,classifiy the training set to two class)
逻辑回归算法
- 此处python实现机器学习的逻辑回归算法(A logical regression algorithm for machine learning by Python)
Logistic
- 逻辑回归模型的实现，最易理解的分类器，适合小样本数据集(The realization of logistic regression model, the most understandable classifier, is suitable for small sample datasets.)
machine_learning_python-master
- 通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。感知机的基本形式和对偶形式的实现 Kmeans和Kmeans++的实现 EM GMM高斯混合和GMM+LASSO的实现 实现朴素贝叶斯的基本算法和高斯混合朴素贝叶斯算法 实现决策树的基本算法 实现adaboost基本算法 实现svm基本算法 实现逻辑回归基本算法(By reading the data codes on the I
Logistic回归统计算法的matlab实现
- 统计回归分析，逻辑斯蒂多元线性回归，机器学习，详细代码解说，机器训练等(Logistic multiple linear regression, statistical regression analysis, logistic multiple linear regression, machine learning, detailed code interpretation, machine training, etc)