资源列表
Klassifikator
- 这是一个用于诊断故障的贝叶斯分类器，简单易用-bayes classification
PICInMachineLearing
- 在解释机器学习的基本概念的时候，我发现自己总是回到有限的几幅图中。以下是我认为最有启发性的条目列表。-In explaining the basic concepts of machine learning, I found myself always return to limited several figures. The following is a list I think the most inspiring entries.
BigDataAI
- 简要介绍了当前大数据人工智能研究与进展，有借鉴意义-Introduces the current research and development of artificial intelligence, big data, there is reference
SVM
- SVM，实现线性可分二维数据和线性不可分二维数据的分类。svm的应用：垃圾邮件的分类。-SVM, realized linearly separable two-dimensional data and linear inseparable two-dimensional data classification. svm applications: spam classification.
Sparse-Autoencoder
- 稀疏自编码是构成堆栈式自编码的基础，通过稀疏自编码可以提取图像的边缘特征。-Sparse coding constitute the basis of the stack self-encoded by sparse coding can be extracted the edge feature of the image.
TSP-solved-by-ACO
- 求TSP最短路径问题，采用蚁群算法求解，给予C++编程的资料和程序-TSP solved by ACO
ex1
- 贝叶斯方法一篇比较科普的中文介绍可以见pongba的平凡而神奇的贝叶斯方法: http://mindhacks.cn/2008/09/21/the-magical-bayesian-method/，实际实现一个贝叶斯分类器之后再回头看这篇文章，感觉就很不一样。 在模式识别的实际应用中，贝叶斯方法绝非就是post正比于prior*likelihood这个公式这么简单，一般而言我们都会用正态分布拟合likelihood来实现。-pattern identification
netural-network
- 常用神经网络算法，以及原理实现。。。包括MLP神经网络、卷积神经网络-Common neural network algorithm, as well as the principle of implementation... Including MLP neural network, convolutional neural network, etc.
NN
- 改进过的BP算法，有dropout,和weight decay项，可以设置三种激活函数。可以用来分类。-BP had improved algorithm, dropout, and weight decay term, you can set three activation functions. It can be used for classification.
BPni
- 对BP神经网络进行逆向建模，能够对微波器件进行逆向分析，从而进行设计-build up BP neural network inverse modeling to analysis microwave devices and to design them.
psoRBFS110
- 用pso算法优化RBF神经网络，从而对微带线的S11参数进行建模-RBF neural networks were optimized by pso algorithm, thus model for S11 parameters of the microstrip line
VCBP
- XP系统，VC6.0编程环境。验证BP神经网络的函数逼近能力，模拟Sin函数的输出。在View中分别用“-”、“o”代表Sin函数的原输出值和BP的输出值。-XP system,VC6.0. BP network is used to approximate the output of sin function.In th view.cpp, - shows the value of sin function and 0 shows the value of BP network.