fp_growth，apriori算法的升级版，减少了扫描数据库的次数

神经网络C程序的第6章-neural network C procedures for the Chapter 6

模式识别在珠宝鉴定中的应用，该文是基于模式识别应用领域的文档类文献。-Pattern Recognition in jewelry appraisal application, the article is based on pattern recognition applications of the document class literature.

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

清华大学对robocup中kick的研究源码-Tsinghua University s study robocup source of kick

Fuzzy logic system VC classes

神经网络 教程初级 比较简单 全面 程序设计-Neural network tutorial relatively simple primary comprehensive program design

基于隐马尔可夫模型的人脸识别源代码,是作模式识别的最好学习代码-Hidden Markov Model-based face recognition source code for pattern recognition is the best learning code

人工神经网络神经网络的程序和训练数据 解压缩后可以运行的-ANN neural network training procedures and decompress data after the run

免疫算法求解函数优化问题运行时请选择所要的函数,并且点Button1执行-immune function algorithm optimization runtime please select the function and implementation point Button1

Matlab神经网络工具箱应用简介 神经网络已经在各个领域中应用，以实现各种复杂的功能。这些领域包括：模式识别、 鉴定、分类、语音、翻译和控制系统。 如今神经网络能够用来解决常规计算机和人难以解决的问题。我们主要通过这个工具箱 来建立示范的神经网络系统，并应用到工程、金融和其他实际项目中去。-Matlab neural network toolbox application profile neural network has been applied in various fields in order to achieve a variety of complex functions. These areas include: pattern recognition, identification, classification, voice, translation and control systems. Today neural networks can be used to conventional computers and people to solve difficult problems. We mainly through the toolbox to create a model of neural network systems, and applied to the engineering, financial and other practical projects.