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。

c# c++ 贝塞尔曲线, 只有几行代码就可以实现,非常简单,可以在游戏中使用

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(); } }); } }

一提到电子邮件(e．mail)，相信大家都不会感到陌生。随着Intemet的迅猛发展，电子邮件凭借使用方便、快捷、廉价的特点很快被广大网络用户所接受，已成为当前最流行的信息交流方式之一。但是电子邮件给我们带来便利的同时，垃圾邮件也随之产生，带来了巨大的危害。近年来大量的商业、色情、反动垃圾邮件和邮件病毒的泛滥给互联网用户带来很多烦恼和侵害，也给社会带来了极大的负面影响，邮件系统的安全问题引起业界的重点关注。垃圾邮件在国内的情况十分严重，中国如今成为了世界垃圾邮件来源的第三大国，反垃圾邮件迫在眉睫。因此研究垃圾邮件过滤具有着极其重大的现实意义。