资源列表
LLE
- 局部线性嵌入算法（LLE）。它可以很好表达数据的内在流行结构，能够保留数据的本质特征，这样可以较好的保留原有数据特征，其算法也需要进行稠密采样，进行特征参数优化-LLE Algorithm
HW1
- 使用knn 和 condnesed 1nn来实现了一组字母数据的识别-Using knn and cnn algorithm to recognize a letter data
ELM
- 标准极限学习机，结合个人理解在原有版本上改进，使代码更容易理解。同时直接可以在内存中读数据，原有版本需要将数据存为文本读取.matlab版本-Standard extreme learning machine, combined with personal understanding improvement on the original version, make the code easier to understand Can directly read data in the memor
kernel-ELM
- 核极限学习机，引入核函数解决ELM求解问题，一旦参数选定，结果就稳定下来，不再混入随机。在原来版本上加入自己理解改写，使得代码更容易理解。-Kernel extreme learning machine, kernel function is applied to deal with ELM to solve the problem, once the parameters selected, the result is stabilized, not randomly in the origi
SSELM-and-USELM
- 半监督核无监督极限学习机，用于半监督核无监督学习，比传统方法速度略快，且可以直接应用多分类问题-A semi-supervised nuclear unsupervised extreme learning machine, used for a semi-supervised kernel unsupervised learning, slightly faster than the traditional methods, and can direct application classif
Stacked-ELM
- 栈式ELM,堆叠多层ELM实现深度学习，比传统方法快且准确度高-Stacked ELM, stacked multi-layer ELM to realize deep learning, faster than the traditional method and high accuracy
ExtremeSVR
- 文献，极限学习机和SVR的结合，达到高于SVR的回归效果，且实现方便-The combination of extreme learning machine and SVR, achieve higher than that of SVR regression effect, and easy to implement
License-Plate-Recognition
- License Plate Recognition
Chaotic-Systems-Toolbox
- 相空间重构的matlab实现工具包，适合初学者使用-This toolbox contains a set of functions which can be used to simulate some of the most known chaotic systems, such as: - The Henon map - The Ikeda map - The Logistic map - The quadratic map - The Lorentz fl
Neural-network-MATLAB-simulation-
- 神经网络与matlab 一本很好的神经网络书籍-Neural network model and MATLAB simulation program design
BAT
- 标准的蝙蝠优化算法,通过模仿蝙蝠用超声波搜索猎物的过程来寻求最优解，属于一种新颖的智能优化算法-Standard bat optimization algorithms
MKAD
- 多核的异常检测（MKAD）算法用于一组文件的异常检测。它引入多喝到一个单优化函数，并使用一类支持向量机（OCSVM）框架进行实现-The Multiple Kernel Anomaly Detection (MKAD) algorithm is designed for anomaly detection over a set of files. It combines multiple kernels into a single optimization function using the