搜索资源列表
NBC
- 朴素贝叶斯机器学习，用来分类文本，matlab编程，有测试和训练两部分。-Naive Bayesian machine learning to classify text, matlab programming, testing, and training has two parts.
bayes
- 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
nativeBayes
- 朴素贝叶斯算法的java代码实现与实际例子-native beyes
naivebayes
- 用matlab编写的朴素贝叶斯分类器程序，以文件中的训练数据对分类器进行训练，并用测试数据进行测试，以验证分类器的性能。-The algorithm for naive bayes classifier.
NaiveBayesian
- 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
bayes
- 基于自然对数改进的朴素贝叶斯,统计TPR,NPR,TFR,TPR-Based on the natural logarithm improved Naive Bayes
Bayes
- 用java语言实现的朴素贝叶斯算法。可以根据输入属性值来判断目标属性的值。-This is the code about Naive Bayes.
TestNaiveBayes
- 采用朴素贝叶斯算法自动处理大量数据，Java语言编写。数据见TXT文件。-Using naive Bayesian algorithm to automatically deal with a large number of data, Java language. See data TXT file.
AI-Naive
- 利用Python实现朴素贝叶斯分类方法。实现程序具有普适性，同时附带测试数据。可以直接运行。-Python implementations utilizing Naive Bayes classification. Achieve universal program has also included with the test data. It can be run directly.
bayes1
- 朴素贝叶斯法主要根据概率论中的贝叶斯法则，是一种很好的用于文本分析的机器学习算法-Naive Bayes method is a kind of machine learning algorithm based on the theory of probability, which is a good machine learning algorithm for text analysis.
textclassify.tar
- 简单的文本分类，用python实现了朴素贝叶斯和SVM-Simple text classification, realized with python Naive Bayes and SVM
Naive-Bayes
- Naive Bayes 朴素贝叶斯算法的实现-Naive Bayes
MachineLearning
- 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
bys
- 本文主要描述了朴素贝叶斯分类方法，包括模型导出和学习描述。实例部分总结了《machine learning in action》一书中展示的一个该方法用于句子感情色彩分类的程序。1 方法概述 学习（参数估计） 实现：朴素贝叶斯下的文本分类-This paper describes the naive Bayesian classification methods, including export and learning descr iptive model. Example
test1
- 朴素贝叶斯分类算法，用于进行文本分类，自带训练集-Naive Bias classification algorithm with training set
python-code-for-Machine-learning
- 用于机器学习的全方位python代码，包括K-近邻算法、决策树、朴素贝叶斯、Logistic 回归 、支持向量机、利用 AdaBoost 元算法提高分类性能、预测数值型数据：回归、树回归、利用 K-均值聚类算法对未标注数据分组、使用 Apriori 算法进行关联分析、使用 FP-growth 算法来高效分析频繁项集、利用 PCA 来简化数据、利用 SVD 简化数据、大数据与 MapReduce-The full range of python code for machine learning
CLASSIFICATION-with-newsgroup
- 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,
JAbbbb
- 改进了基于朴素贝叶斯的java实现短信过滤算法-java achieve SMS filtering .............................................. ...........................
weka
- 机器学习调用weka的jar包实现的源码，包含朴素贝叶斯,决策树,ID3,以及特征选择的源码，数据集使用weka的数据集，需要使用arff文件读入。-Weka machine learning to call the jar package implements the source, including Naive Bayes, decision trees, ID3, and features selected source dataset weka data set, you need t
Main
- 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.