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朴素贝叶斯算法
求导致某一结果或现象发生的最可能的条件-Naive Bayes algorithm for the most likely cause of the condition or a result of the phenomenon
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机器学习中的朴素贝叶斯算法,利用python实现的算法-The naive Bayesian algorithm in machine learning, using Python to achieve the algorithm
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使用Python语言写的经典朴素贝叶斯算法的实现,完全能够应对算法设计课程的课程设计的代码需要-Implemented using Python language written in classic Naive Bayes algorithm, fully able to cope with the algorithm design course curriculum design code requires
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multi class naive bayes algorithm i coded for predicting football results
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非常好用的基于QT实现机器学习的朴素贝叶斯算法-Very easy to use machine learning based on QT implement Naive Bayes algorithm
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多文本分类,KNN和朴素贝叶斯算法,英文文本,-Text categorization, KNN and naive bayes algorithm, the English text,
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朴素贝叶斯算法的代码实现,能够实现对数据的自相关,互协方差等统计方面特征的数学分析-Code naive Bayes algorithm implementation can be achieved for autocorrelation of the data, mathematical analysis statistical characteristics of the cross-covariance, etc.
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支持向量机与朴素贝叶斯算法,对数据进行分类后深度了解数据的结构-Support vector machine and naive Bayes algorithm.Classifying the data and understanding the structure of the data in depth
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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.
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朴素贝叶斯文本分类的简单案例,了解朴素贝叶斯的算法实现过程(Naive Bayes text classification of simple cases, to understand the naive Bayes algorithm implementation process)
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In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
Naive Bayes has been studied extensively since the 19
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朴素贝叶斯算法分类及回归,附带训练集和测试集,可以评测正确率和输出预测文件(Classification and regression of naive Bayes algorithm, incidental training set and test set can evaluate the correct rate and output prediction file.)
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朴素贝叶斯算法,是十大最经典的数据挖掘算法之一(Naive Bayes algorithm, the most classic data mining algorithm)
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借助朴素贝叶斯算法,针对文本正负面进行判别,并且利用C#进行编程实现(The naive Bayes algorithm is used to judge the positive and negative sides of the text, and the program is implemented by using C#)
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此处python实现机器学习朴素贝叶斯算法(Here Python implements the naive Bayes algorithm for machine learning)
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本报告对 朴素贝叶斯模型及线性判别分析、二次判别分析 进行系统测试
“生成模型”是机器学习中监督学习方法的一类。与“判别模型”学习决
策函数和条件概率不同,生成模型主要学习的是联合概率分布??(??,??)。本
文中,我们从朴素贝叶斯算法入手,分析比较了几种常见的生成模型(包
括线性判别分析和二次判别分析)应用于多因子选股的异同,希望对本领
域的投资者产生有实用意义的参考价值。(This report gives a systematic test of naive Bayesian
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朴素贝叶斯算法运行和程序代码的实验与结果(Operation of naive Bayes algorithm)
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朴素贝叶斯算法的相关资料,包含算法以及实验结果等。(The related data of the naive Bayes algorithm.)
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机器学习入门经典算法中的朴素贝叶斯算法,python3.6,编译通过可运行。(Naive Bayes algorithm in machine learning classic algorithm)
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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 Internet, we can process oursel
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