资源列表
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.
Collaborative-fuzzy-clustering
- 协同模糊聚类算法,这是聚类算法的一种。国内研究的人不多。-Collaborative fuzzy clustering,
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.
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.
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
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
self-taught-learning
- 自主学习把稀疏自编码器和分类器实现结合。先通过稀疏自编码对无标签的5-9的手写体进行训练得到最优参数，然后通过前向传播，得到训练集和测试集的特征，通过0-4有标签训练集训练出softmax模型，然后输入测试集到分类模型实现分类。-Independent Learning the encoder and the sparse classifiers achieve the combination. First through sparse coding since no label was ha
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.
stackedAE
- 堆栈自编码，通过两个稀疏自编码的堆叠和softmax分类模型，实现手写体的分类。-Stack self-encoding, since encoding by two sparse stack and softmax classification model to classify handwriting.
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.
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
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.