神经网络设计书籍配套源码（matlab平台）-neural network design books matching source (Matlab platforms)

一个关于HMM(隐马尔可夫模型)实现的C语言库。是Visual C++数字图像模式识别技术及工程实践的配套源代码-one of HMM (HMM) with the C library. Yes Visual C digital image pattern recognition technology and engineering practice supporting source code

基于Fisher准则线性分类器设计，理解Fisher准则方法确定最佳线性分界面方法的原理，以及Lagrande乘子求解的原理。-Based on Fisher linear classifier design guidelines, understanding the criteria Fisher method to determine the best linear method of the principle of the interface, as well as solving Lagrande multiplier principle.

这是一个简单的人工智能的例子 希望大家能以此为作出更好的 更完善的

本程序代码是人工智能中九宫搜索的解决 采用Java语言-the code is AI JiuGongTu search solutions using Java language

用BP神经网络逼近函数Z=SIN(X*Y),采用C++编程.-Using BP neural network to approach the function Z = SIN (X* Y), using C++ programming.

本程序实现的是关联规则挖掘领域中规则发现算法.-this program is the Mining Association Rules field rules algorithm.

JESS开发模式，人工智能开发架构-JESS development model, the development of artificial intelligence structure

这是用matlab编写人脸检测软件，是用的adaboost算法，参考价值还是很高的-using Matlab is prepared Face detection software is used adaboost algorithm, reference value or high

提出了基于最小化估计的泛化误差界非优化估计方法。目前大多数的核参数选择方法都是通过极小化了来得到最优参数值，但是求解优化问题的计算代价相当的大，并且不能很好地体现数据的分布特征。本文采用非优化技术，通过极小化泛化误差来优化核及相关参数，由于直接计算最小半径和最大间隔，避免了对优化问题的直接求解，因此可以很好地降低计算代价。并且该方法直接从样本出发，可以很好地体现数据的分布特征，不管数据分布是否均匀都可以适用。给出了基于凸包估计的SVM核选择的模型及实现算法。 -Proposed based on minimizing the estimated generalization error bound of the non-optimal estimation method. Most of the nuclear parameter selection methods are to get the best by minimizing the parameter values, but the computational cost for solving optimization problems is quite large, and can not properly reflect the distribution characteristics of the data. In this paper, the non-optimization technology, by minimizing the generalization error to optimize the nuclear and related parameters, due to the direct calculation of the minimum radius and maximum interval, avoiding the direct solution of the optimization problem, it can very well reduce the computation cost. And that the method directly from the sample one can well reflect the distribution chara