这是通过贝叶斯进行图像分类的代码，老师给的图片集共1000张，最终通过贝叶斯算法对图片训练预测分类，并输出混淆矩阵，召回率，F1，精确率。相关图片文件路径需自行修改。

使用matlab训练基本的神经网络，数据是使用的6类气体的数据，共有3600个，分别测试了7个分类器的性能

模式识别实验报告实验一Bayes分类器设计

bayes_drt bayes_drt是一个Python软件包，用于反转电化学阻抗谱（EIS）数据以获得弛豫时间（DRT）的分布和/或扩散时间（DDT）的分布。 bayes_drt实现了分层的贝叶斯模型，以提供经过精确校准的DRT或DDT估计，而无需进行临时调整。 该软件包提供了两种方法来求解模型： 汉密尔顿蒙特卡洛（HMC）采样以估计后验分布，同时提供分布的点估计和可信区间 L-BFGS优化可最大化后验概率，从而提供分布的最大后验（MAP）点估计 使用这些方法，还可以执行多分布反演，例如同时安装DRT和DDT。 这是一项实验性功能，需要进行一些手动调整。 有关示例，请参见教程。 该软件包还提供了普通的和超参数的岭回归方法，这可能对比较或获得分布的初始估计很有用。 超参数岭回归方法是Ciucci和Chen（ ）开发并由Effat和Ciucci（ ）扩展的方法的实现。 ）。

垃圾邮件分类 K最近邻居分类器： Accuracy: 93.x% 决策树分类器： Accuracy: 93-94% 朴素贝叶斯分类器： Accuracy: 96.x% Ada-Boost分类器： Accuracy: 96.x% 支持向量机： Accuracy: 97.x% 随机森林分类器： Accuracy: 97-98.x% 调整参数可能会导致结果变化

进一步理解和掌握贝叶斯算法的基本原理； 能够使用贝叶斯算法对数据进行分类； 理解掌握最小错误率贝叶斯分类器

Decision trees are particularly promising in symbolic representation and reasoning due to their comprehensible nature, which resembles the hierarchical process of human decision making. However, their drawbacks, caused by the single-tree structure, cannot be ignored. A rigid decision path may cause the majority class to overwhelm other class when dealing with imbalanced data sets, and pruning removes not only superfluous nodes, but also subtrees. The proposed learning algorithm, flexible hybrid decision forest (FHDF), mines information implicated in each instance to form logical rules on the basis of a chain rule of local mutual information, then forms different decision tree structures and decision forests later. The most credible decision path from the decision forest can be selected to make a prediction. Furthermore, functional dependencies (FDs), which are extracted from the whole data set based on association rule analysis, perform embeddedattribute selection to remove nodes rather than subtrees, thus helping to achieve different levels of knowledge representation and improve model comprehension in the framework of semi-supervised learning. Naive Bayes replaces the leaf nodes at the bottom of the tree hierarchy, where the conditional independence assumption may hold. This techniquereduces the potential for overfitting and overtraining and improves the prediction quality and generalization. Experimental results on UCI data sets demonstrate the efficacy of the proposed approach.

MATLAB工具箱大全-贝叶斯网工具箱Bayes Net Toolbox(BNT)

Matlab高斯朴素贝叶斯算法和KNN分类算法的实现。 培训和测试数据取自UCI机器学习数据存储库的“玻璃识别数据集”。数据集在Data文件夹下 注意：为了进行KNN的准确性计算，使用了留一法交叉验证。

基于jupyter的贝叶斯模型-bayes.zip