的计算机模式识别技术

介绍了神经网络模式识别的基本知识，适合于模式识别领域的人员-it introduces the ABC of the pattern recognation of NN, it is for the people of pattern recognation

计算机网络经典算法：生产者与消费者问题的源代码-classic algorithm of computer network:producer and consumer

详细介绍了局部奇异值分解-details of local SVD

svm Main_SVR_Epsilon 用于svm回归的 在matlab中运用 -svm regression svm Main_SVR_Epsilon for use in matlab

在vc上运行的回答集求解器源代码，能够供在人工智能上学习的学习者使用-Running in vc answer set solver source code, for use in artificial intelligence to learn the use of the learners

计算机模式识别技术-the technology of the computer pattern recognation

可以自动生成qbf问题实例,但需要给定一些参数.-can produce QBF instance

该程序比较人性化，有个友好界面，能加载图像，并根据图像训练Hopfield神经网络，你也可以通过添加噪声给图像，以判断它的识别率。-Comparison of the program user-friendly, has a friendly interface that can load images, and in accordance with the training images Hopfield neural network, you can also add noise to images, to determine its recognition rate.

FP-GROWTH算法的源代码(JAVA)-FP-Growth code and algrithem

GENNET是一个通过遗传算法优化权重的16个神经元的全连接神经网络。训练目的是使网络在一定假设的激励下，随时间产生同目标函数同样的响应，即:可以把此网络制作为一个任意波形的信号发生器。 程序分3个示例说明遗传算法进化过程中网络输出的变化趋势。可以看出网络输出（蓝色曲线）随着时间变化逐渐逼近目标函数（红色曲线）。此程序不仅可以学习遗传算法，也可以用于研究全连接网络的直观示例。-GENNET is a genetic algorithm to optimize the weights by the 16 neurons fully connected neural networks. Training purposes is to enable network inspired by certain assumptions, over time, produce the same response with the objective function, namely: the network can be produced as an arbitrary waveform signal generator. Procedure, three examples illustrate the evolution of genetic algorithms in the network output trends. We can see that the network output (blue curve) changes over time, gradually approaching the objective function (red curve). This program can not only learn from the genetic algorithm can also be used to study visual example of a fully connected network.