sacredno4的专栏

包含很多知名算法实现，支持向量机，决策树，粗糙集，贝叶斯分类器等 http://rsproject.mimuw.edu.pl/ 不可用于商业目的

该方法详细讲解了如何在SQL中实现K means

The Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman.

The Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman. 统计学习经典书，2009年新版

用python写的一段贝叶斯网络的程序 This file describes a Bayes Net Toolkit that we will refer to now as BNT. This version is 0.1. Let's consider this code an "alpha" version that contains some useful functionality, but is not complete, and is not a ready-to-use "application". The purpose of the toolkit is to facilitate creating experimental Bayes nets that analyze sequences of events. The toolkit provides code to help with the following: (a) creating Bayes nets. There are three classes of nodes defined, and to construct a Bayes net, you can write code that calls the constructors of these classes, and then you can create links among them. (b) displaying Bayes nets. There is code to create new windows and to draw Bayes nets in them. This includes drawing the nodes, the arcs, the labels, and various properties of nodes. (c) propagating a-posteriori probabilities. When one node's probability changes, the posterior probabilities of nodes downstream from it may need to change, too, depending on firing thresholds, etc. There is code in the toolkit to support that. (d) simulating events ("playing" event sequences) and having the Bayes net respond to them. This functionality is split over several files. Here are the files and the functionality that they represent. BayesNetNode.py: class definition for the basic node in a Bayes net. BayesUpdating.py: computing the a-posteriori probability of a node given the probabilities of its parents. InputNode.py: class definition for "input nodes". InputNode is a subclass of BayesNetNode. Input nodes have special features that allow them to recognize evidence items (using regular-expression pattern matching of the string descriptions of events). OutputNode.py: class definition for "output nodes". OutputBode is a subclass of BayesNetNode. An output node can have a list of actions to be performed when the node's posterior probability exceeds a threshold ReadWriteSigmaFiles.py: Functionality for loading and saving Bayes nets in an XML format. SampleNets.py: Some code that constructs a sample Bayes net. This is called when SIGMAEditor.py is started up. SIGMAEditor.py: A main program that can be turned into an experimental application by adding menus, more code, etc. It has some facilities already for loading event sequence files and playing them. sample-event-file.txt: A sequence of events that exemplifies the format for these events. gma-mona.igm: A sample Bayes net in the form of an XML file. The SIGMAEditor program can read this type of file. Here are some limitations of the toolkit as of 23 February 2009: 1. Users cannot yet edit Bayes nets directly in the SIGMAEditor. Code has to be written to create new Bayes nets, at this time. 2. If you select the File menu's option to load a new Bayes net file, you get a fixed example: gma-mona.igm. This should be changed in the future to bring up a file dialog box so that the user can select the file. 3. When you "run" an event sequence in the SIGMAEditor, the program will present each event to each input node and find out if the input node's filter matches the evidence. If it does match, that fact is printed to standard output, but nothing else is done. What should then happen is that the node's probability is updated according to its response method, and if the new probability exceeds the node's threshold, then its successor ("children") get their probabilities updated, too. 4. No animation of the Bayes net is performed when an event sequence is run. Ideally, the diagram would be updated dynamically to show the activity, especially when posterior probabilities of nodes change and thresholds are exceeded. To use the BNT, do three kinds of development: A. create your own Bayes net whose input nodes correspond to pieces of evidence that might be presented and that might be relevant to drawing inferences about what's going on in the situation or process that you are analyzing. You do this by writing Python code that calls constructors etc. See the example in SampleNets.py. B. create a sample event stream that represents a plausible sequence of events that your system should be able to analyze. Put this in a file in the same format as used in sample-event-sequence.txt. C. modify the code of BNT or add new modules as necessary to obtain the functionality you want in your system. This could include code to perform actions whenever an output node's threshold is exceeded. It could include code to generate events (rather than read them from a file). And it could include code to describe more clearly what is going on whenever a node's probability is updated (e.g., what the significance of the update is -- more certainty about something, an indication that the weight of evidence is becoming strong, etc.)

Probability and Random Processes配套习题，英文版，需要djvu阅读器，请搜索csdn下载频道

英文版，需要djvu阅读器，请搜索csdn下载频道