人工智能实验三朴素贝叶斯C++.zip

在VC6.0编译环境下使用C++编写的朴素贝叶斯分类程序

vc++ 包含Ling-Spam 特征库和先验概率的计算 提取垃圾邮件的中的核心词汇 提取正常邮件的中的核心词汇 反馈机制

包含25封正常邮件、25封垃圾邮件以及分类器源代码，适合ML初学者使用

内容包括朴素贝叶斯算法python实现代码，实现对iris分类，包含iris的txt格式的数据集。

faceRecgSys：使用Matlab的人脸识别系统； 算法：LBP，PCA，KNN，SVM和朴素贝叶斯

Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. In this survey, we provide an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are: 1) it provides an elegant approach to action-selection (exploration/ exploitation) as a function of the uncertainty in learning; and 2) it provides a machinery to incorporate prior knowledge into the algorithms. We first discuss models and methods for Bayesian inference in the simple single-step Bandit model. We then review the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. We also present Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. The objective of the paper is to provide a comprehensive survey on Bayesian RL algorithms and their theoretical and empirical properties.

线性高斯分布： P(c | h, subsidy) = N(ath + bt, t2)(c) = 1/ (t21/2) e –1/2{[c-(ath + bt)]/t]} P(c | h, ~subsidy) = N(afh + bf, f2)(c) = 1/ (f21/2) e –1/2{[c-(afh + bf)]/t]} S型函数(Sigmoid function) p(buys | Cost = c) = 1 / {1 + exp[-2(-u+)/ ]}

贝叶斯分类算法是统计学的一种分类算法，利用概率统计对数据进行分类，含数据集，Python实现贝叶斯分类算法

package subjectivebayes; import java.awt.Toolkit; import javax.swing.SwingUtilities; import javax.swing.UIManager; import java.awt.Dimension; /** * Title: * * Description: * * Copyright: Copyright (c) 2010 * * Company: * * @author not attributable * @version 1.0 */ public class MyApp { boolean packFrame = false; /** * Construct and show the application. */ public MyApp() { EnterBayes frame = new EnterBayes(); // Validate frames that have preset sizes // Pack frames that have useful preferred size info, e.g. from their layout if (packFrame) { frame.pack(); } else { frame.validate(); } // Center the window Dimension screenSize = Toolkit.getDefaultToolkit().getScreenSize(); Dimension frameSize = frame.getSize(); if (frameSize.height > screenSize.height) { frameSize.height = screenSize.height; } if (frameSize.width > screenSize.width) { frameSize.width = screenSize.width; } frame.setLocation((screenSize.width - frameSize.width) / 2, (screenSize.height - frameSize.height) / 2); frame.setVisible(true); } /** * Application entry point. * * @param args String[] */ public static void main(String[] args) { SwingUtilities.invokeLater(new Runnable() { public void run() { try { UIManager.setLookAndFeel(UIManager. getSystemLookAndFeelClassName()); } catch (Exception exception) { exception.printStackTrace(); } new MyApp(); } }); } }