专家浅谈人工智能，doc文档

关于语音情感识别的一篇论文，里面描述了人工神经网络的方面-Speech Emotion Recognition on a paper, which describes aspects of artificial neural network

将遗传算法(GA)与传统SVM算法结合,构造出一种参数最优的进化SVM(GA2SVM),SVM 模型采用径向基函数(RBF)作为核函数，利用格雷码编码方式对SVM算法的模型参数进行遗传编码和优化搜索，将搜索到的优化结果作为SVM 的最终模型参数。-Genetic algorithm (GA) combined with the traditional SVM algorithm, a kind of tectonic evolution of the optimal parameters of SVM (GA2SVM), SVM model using Radial Basis Function (RBF) as kernel function, the use of Gray code encoding algorithm of the SVM model parameters of genetic coding and optimization of search, will search for the optimal results as the final SVM model parameters.

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

国内的一个A*算法演示，可以自己画地图，演示效果非常好-domestic A* algorithm for a demonstration, can you paint maps, the effect was very good demonstration

隐马尔可夫模型算法(解压后为ppt文件，改名为a.ppt)-HMM algorithm (decompression after ppt documents, renamed a.ppt)

数据挖掘中很不错的源码,一个fss算法的java源程序,有极好的可读性-data mining is a good source, a fss algorithm java source files, has an excellent readability

该文档是清华大学的模式识别教程，该版本是网页版本，浏览非常方便，内容也非常详细，非常适合初学者，个人举得特别是支持向量机那一章描述很棒！-this document is from Tsinghua University,this is the html version,it is convenient to read, it is very suitable for the elementary readers,in my own opinion,the charter of svm is very detail and easily understandable.

遗传算法在matlab中的实现-genetic algorithm in the realization of Matlab

人工鱼群算法（AFSA）是2002年李晓磊提出的基于鱼群行为的寻求全局最优 的新型搜索策略，该算法具有较优的全局收敛能力及较快的寻优速度。本文首次将 人工鱼群算法应用于人工神经网络的学习，形成了人工鱼群神经网络模型，通过与 BP算法、模拟退化算法、进化算法训练的人工神经网络进行比较，验证了人工鱼群 神经网络在全局寻优能力上的优势，进而利用人工鱼群神经网络进行电力系统短期 负荷预测，建立了人工鱼群神经网络预测模型。为了进一步提高算法的稳定性，以 及求得全局最优值的能力，文中给出了改进的人工鱼群算法。当人工鱼种群的最优 值在多代未变的情况下，加入了“跳跃”行为，改变人工鱼个体的参数，避免陷入 局部最优并提高了寻求全局最优解的能力。然后建立了新的预测模型，实际编程表 明，改进后的模型具有更好的全局最优化能力及稳定性。-err

最新的支持向量机工具箱，有了它会很方便 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999. -The newest work tools of svm,it will be very convenient to have it.