搜索资源列表
spider1
- spider，很好用的模式识别工具箱，里面有各种分类工具，从有监督学习到无监督学习，从模型选择到参数选择。而且也将各个方法封装成类，使用方便。-spider, good use of pattern recognition toolbox, there are various classification tools, from supervised learning to unsupervised learning, choose Preferences from the model. But
Neurons-PID-control
- 采用四种埪制律进行单神经元PID控制，即无监督的Hebb学习规则；有监督的Delta学习规则；有监督的Hebb学习规则；改进的Hebb学习规则。-Kong legal system of four single-neuron PID control, the Hebb learning rule is unsupervised a supervised learning rule Delta a supervised Hebb learning rule modified Hebb lea
manifold-learning.lle
- 流形学习是一种新的无监督机器学习方法，局部线形嵌入（Locally Linear Embedding，LLE）发现嵌入在高维数据中的低维流形，流形学习算法,MATLAB程序-manifold learning
cwscore
- 主成分分析matlab程序，里面包含子程序，运用时直接调用主程序即可，可用于无监督模式学习-Principal component analysis matlab program, which contains a subroutine, the main program can be called directly when applying, can be used for unsupervised learning mode
k_means
- 经典的k均值聚类算法，用于无监督学习和数据分类，广泛应用于数据处理和模式识别领域-Classical k-means clustering algorithm for unsupervised learning and data classification, widely used in data processing and pattern recognition
kmeans
- 基于k均值的无监督聚类算法，输出有各个样本的类别标签，目标函数在每次迭代后的值，聚类中心以及聚类区间。内有测试数据，点击 test.m 可以完美运行。(The unsupervised clustering algorithm based on K means outputs the class labels of each sample, the value of the target function after each iteration, the clustering center a
machine-learning-ex7
- 吴恩达机器学习课程，第八周作业，详解如何实现PCA以及kmeans(Andrew NG machine learning course, eighth weeks of assignment, detailed how to implement PCA and kmeans)
kernel-regression-master
- kernel方法，进行半监督学习，数据分类与识别，有标签与无标签(KERNELmethod,semi-supervised learning,classification,label unlabel)
kmeans聚类算法
- kmeans聚类分析，无监督学习实现Matlab代码(Kmeans clustering analysis, unsupervised learning implementation of MATLAB code)
线性判别分析(linear discriminant analysis)
- LDA是一种监督学习的降维技术，也就是说它的数据集的每个样本是有类别输出的，这点和PCA不同。PCA是不考虑样本类别输出的无监督降维技术。