自适应共振理论（艺术）

这个主要介绍会计中软件的应用和会计电算法，里面含有一些讲稿和题目-This focuses on the application of accounting and accounting software, power algorithm, which contains a number of lectures and topics

HMM用于人脸识别，从OPENCV里提取出来的,非常经典的说-HMM for face recognition, OPENCV extract from the very classical said

文件中包含某老师关于智能优化计算课程中的遗传算法部分的讲义，其中包含详细的例子及源代码。-File contains a calculation of the teachers on the intelligent optimization of the genetic algorithm curriculum part of the lecture, which includes detailed examples and source code.

美国麻省理工大学教授人工智能的课件以及课后的习题相关-American University professor of artificial intelligence at MIT courseware and the exercises related to the after-school

机器人路径规划演示平台，自主移动的智能机器人演示程序源代码，可以实现机器人壁障功能-Demonstration platform for robot path planning, intelligent autonomous mobile robot demo program source code, you can realize robot barrier function

用mfc写的一个五子棋游戏，不含人工智能-mfc written with a 331 game, which was non-AI

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

ANN And Hopfield Neural Network

这个代码是用lib-svm来实现svm分类器的，简单易懂，并且有详细的注释，对于刚刚接触SVM的初学者有很大的帮助，一看就能上手，我也是新手，所以应该还是很有帮助的。-This code is used to achieve lib-svm classifier svm, easy-to-read, and detailed notes, just contact SVM for the great help for beginners, a look will be able to start, I am also a novice, so it should be or very useful.

人工智能领域全新的研究结果-new areas of artificial intelligence research