此编码是一个数据挖掘的决策树各种算法。以作为入门提示

命题逻辑中的推理规则可以用来证明一个已经表示成命题公式的定理，但它还不便于机械化实施。因为其中的各条规则都未指明在推导的什麽步骤上应该引入什麽前提或中间结论，这些还有赖于人的经验技巧和才能。1960年王浩提出的算法，可以完全脱离人的经验和技巧，机械地构造每一步推导，证明一个命题逻辑定理的成立。 -Propositional logic in the inference rule can be used to prove that a propositional formula has been expressed as the theorem, but it is not easy to implement mechanization. Because the rules did not specify what steps in the derivation of what should be the introduction of the premise or intermediate conclusions, which also depends on the person s experience of the skills and talent. Wang Hao in 1960 proposed algorithm can be completely divorced from the person s experience and skills, a mechanical structure is derived for each step to prove that a propositional logic theorem establishment.

Learning to Rank的一个方法，把排序问题转换成了一个分类问题，然后用支持向量机（SVM）训练出一个模型来。-Learning to Rank of a way to sort problem is transformed into a classification problem, and then use support vector machine (SVM) to train a model.

人工智能的经典八皇后问题,这个算法是a*算法.利用这个算法可以解决八数码,八皇后问题等 -artificial intelligence Queen"s classic 8, the algorithm is a* algorithm. Use of this algorithm can solve eight digital, 8 Queen"s problems

《神经网络模式识别及其实现》源程序 几个VC编写的神经网络示例-neuronetwork

c++的程序 有关神经网络的,请站长加我,-c procedures of the neural network, for my head, thank you

基于MATLAB完成的神经网络源程序 大家-Based on the MATLAB neural network to complete the U.S. source

一个利用C#语言实现的遗传算法源程序-a C# language of the genetic algorithm source

用禁忌搜索解决TSP问题，程序中使用对路径长度禁忌的方法，运行效率比较高。-Tabu search solution TSP, procedures for the use of the taboo path length, operating more efficient.

专业介绍svm算法的书籍，适合有高等数学，线性代数，矩阵论，和数里统计基础的读者，他是一本全面介绍svm的书籍，从数学基础到应用，是svm学习者的一本好书。-Introduction SVM algorithm professional books, suitable for advanced mathematics, linear algebra,矩阵论, and several readers in the statistical basis, he is a comprehensive introduction to SVM books, from mathematical foundations to applications, is an SVM learner Good Books.

该程序包包含三个函数，一个用于建立BP神经网络，一个用于训练该网络，最后一个用于测试样本。-The package contains three functions, one for the establishment of BP neural networks, one for training the network, the last one for the test samples.