Principles of Data Mining is a data mining inside another in this classic textbo...

C# 15数码算法，A star 函数实现 -C# 15 Digital Algorithm

本人编写的GramSchmidt神经网络算法，和传统的神经网络算法比较，该算法能自动产生神经元，节约时间，速度快！ 本人还加了自动产生分类的方法和适合各种函数的转换，该zip包括两个文件，一个是GramSchmidt正交分解，是个子函数，主函数是orth_GramSchmidt，放在同一目录下，就可运行，可以对benchmark的逼近和分类问题提供解决方案-I prepared GramSchmidt neural network algorithm, and the traditional neural network algorithm, the algorithm can automatically generate neurons, saving time, fast! I also added a method of automatically generated classification and conversion for a variety of functions, the zip includes two documents, one is GramSchmidt orthogonal decomposition is a sub-function, the main function is orth_GramSchmidt, on the same directory, you can run can benchmark approximation and classification problems to provide solutions

这个程序是个神经网络提供学习能力的实例，是前一个simpleann的功能加强版。-this procedure is a neural network learning ability example is the former simpleann an enhanced version of the function.

根据 trilearn_base_sources-3.3 (一般称做UVA底层)修改的robocup球队代码,对某些模块进行了详细的注释(例如BasicPlayer,WorldMode模块),并且进行了一些基本的动作添加的示范(增加了射门动作,四处看动作,截球动作和贴身带球动作),示范了简单的高层决策修改和队型的修改,书写了一套完整的守门员高层代码,希望对大家有帮助-According to trilearn_base_sources-3.3 (generally known as UVA bottom) to amend the code of RoboCup teams, for certain modules in detail the Notes (such as BasicPlayer, WorldMode module), and carried out some basic moves to add a model (an increase of shooting action , look around action, action and cut the ball moves the ball next to the skin), demonstrated a simple modification of the high-level decision-making and team-based changes, write a complete set of high-level code goalkeeper, in the hope that everyone has to help

4。《演化程序――遗传算法和数据编码的结合》，[英]米凯利维兹着 科学出版社 2000年第一版 本书分三个部分共16章分别介绍了:1.遗传算法的概念、数学原理及方法步骤 2.遗传算法和数据编码联系起来所构成的演化程序 3.演化程序面向一些实际问题的应用。 本书语言生动，结构合理，较少使用专业性术语和深涩词汇，适合面临优化问题的研究生、程序员、设计师、工程师及科研工作人员参考。-4. "Evolutionary process-- genetic coding algorithm and data integration," [E] Mikhail Liweici a 520-531 2000 version of the first book in three parts a total of 16 chapters were introduced : 1. Genetic Algorithm concept mathematical principles and methods of Step 2. genetic algorithms and data coding linked posed by the evolution of procedures 3. Evolution-oriented procedures some of the practical problems of application. The book vivid language, reasonable structure, and less use of professional terminology and vocabulary deep acerbic suitable for optimization problems facing graduate students, programmers, designers, engineers and research personnel.

BP神经网络预测-BP neural network prediction

此为疾病诊断专家系统，用这种结构建立的知识库可以很方便地实现上面所提到的各种功能。这种结 构的最独特之处就是依靠相同的关键（疾病名或其代码）来实现一种疾病与其症候之 间的相互联系。疾病名与症候分别用二叉树进行排序。排序后可由疾病名而查到与该 疾病名有关的所有详细情况，也可由一种症候而查到哪些疾病有这种症候，以及该症 候可能引起各种疾病的概率。-This is the disease diagnosis expert system, using this structure to establish the knowledge base can easily realize the above mentioned features. The structure of the most unique is that relying on the same key (the disease or its code) to achieve a disease and its symptoms between. Name and symptoms of disease were treated with binary tree sort. Sort by the disease after the name of the disease were found with all the details of which can also be a symptom of the diseases that have found such a symptom, as well as the symptoms may lead to the probability of various diseases.

MATLAB神经网络工具箱ＢＰ算法的比较，比较它们的训练时间长短以所达到的效果-MATLAB neural network toolbox BP algorithm compares the length of their training time in order to achieve the effect of the so

数据挖掘k-measextions 算算法，改进了其中部分结构。-Data Mining k-measextions counting algorithm, to improve some part of the structure.

贝叶斯算法是基于贝叶斯定理 P(H|X) = P(X|H)P(H) / P(X).。对于多属性的数据集，计算 P(X|Ci) 的开销非常大，为减低计算复杂度，我们做条件独立的假设，即给定元组的类标号，假定属性值有条件地相互独立，即在属性间不存在依赖关系。此程序仅为算法的一个实现，根据训练数据训练分类器-Bayesian algorithm is based on the Bayes theorem P (H | X) = P (X | H) P (H)/P (X).. For multi-attribute data sets, computing P (X | Ci) of the overhead is very large, in order to reduce the computational complexity, we do conditional independence assumption that a given tuple class label, it is assumed that property values conditionally independent of each other, that does not exist in the inter-attribute dependencies. This procedure is only an implementation of algorithm, according to training data classifier training