搜索资源列表
PCA-SVM
- 在PYTHON里面，采用LIBSVM，实现对TE数据的多类故障的分类。-In PYTHON inside, using LIBSVM, TE data to realize the classification of many types of failures.
kpca_py
- python实现的PCA，即Python scr ipt for kernel PCA with and without CUDA,效率非常好-python implementation of the PCA, Python the scr ipt for kernel the PCA with and without CUDA, the efficiency is very good
pca
- 利用python实现主成分分析，主要用在图像处理等领域-Use python to achieve the principal component analysis, mainly used in the field of image processing, etc.
pca
- pca算法的python实现以及相关实验数据-machine learning pca
PCA-Python
- 用Python语言实现PCA(Principle Component Analysis)。-PCA with python.
PCA
- python PCA算法。 以及主成分分析的相关文档，资料，数据集。-python PCA algorithm.
python-code-for-Machine-learning
- 用于机器学习的全方位python代码，包括K-近邻算法、决策树、朴素贝叶斯、Logistic 回归 、支持向量机、利用 AdaBoost 元算法提高分类性能、预测数值型数据：回归、树回归、利用 K-均值聚类算法对未标注数据分组、使用 Apriori 算法进行关联分析、使用 FP-growth 算法来高效分析频繁项集、利用 PCA 来简化数据、利用 SVD 简化数据、大数据与 MapReduce-The full range of python code for machine learning
PCA
- Python实现PCA将数据转化成前K个主成分的伪码大致如下: ''' 减去平均数计算协方差矩阵计算协方差矩阵的特征值和特征向量将特征值从大到小排序保留最大的K个特征(Python PCA data into pseudo code before the K principal components are as follows: the characteristics of 'average minus the covariance matrix to calculate the covari
js,matlab,python版本的可视化工具
- 高维降低维并可视化，可在网页运行，类似于pca但性能更好
kernel_eca-master
- Kernel Entropy Component Analysis,KECA方法的作者R. Jenssen自己写的MATLAB代码，文章发表在2010年5月的IEEE TPAMI上面-Kernel Entropy Component Analysis, by R. Jenssen, published in IEEE TPAMI 2010.(We introduce kernel entropy component analysis (kernel ECA) as a new method fo
face-SVM
- 用PCA和SVM实现人脸识别，是经典的人脸识别Python代码(Face recognition using PCA and SVM)
face-Adaboost
- 用Adaboost和PCA算法实现人脸识别，用Python写的代码，根据经典的PCA和SVM算法改编(Adaboost and PCA algorithm for face recognition, code written in Python, adapted from the classic PCA and SVM algorithm)
pca算法实现
- 通过Python实现了PCA数据降维的方法(The method of reducing the dimension of PCA data through Python)
ChineseChuLi
- 中文文本处理的python程序，包括分词、删除特殊字符、删除停用词、爬虫程序、PCA降维、Kmean聚类、可视化等(Python programs for Chinese text processing, including participle, deleting special characters, deleting disuse words, crawler programs, PCA dimensionality reduction, Kmean clustering, visuali
机器学习常用方法
- 机器学习常用方法的python实现，包括PCA，随机森林，决策树，层次聚类，kmeans，KNN，线性感知机等(Python implementation of common machine learning methods, including PCA, random forest, decision tree, hierarchical clustering, kmeans, KNN, linear perceptron, etc.)
python_face_recog
- 基于python+opencv 的 人脸识别，对一段视频进行读取，并检测出人脸，然后进行PCA 降维，最后用SVM进行人脸识别，识别率94%左右。(Based on python + opencv face recognition, a video was read, and face detection, and then PCA dimension reduction, and finally SVM face recognition, recognition rate of about 9
k_means
- 主成分分析（Principal Component Analysis，PCA）， 是一种统计方法。通过正交变换将一组可能存在相关性的变量转换为一组线性不相关的变量，转换后的这组变量叫主成分。(It is a statistical method. Through orthogonal transformation, a set of variables that can be correlated can be transformed into a group of linearly irrel
PLSPCAdfunction
- pls和pca的代码，python编写，添加数据文件后，修改输入变量的参数，即可直接运行。(Pls and PCA code, python write, add data files, modify the parameters of the input variables, you can run directly.)
PLSPCAT2andspe
- 故障检测，分别是pls和pca，计算spe和t2控制量。(Fault detection is pls and PCA, respectively, to calculate SPE and T2 control.)
PCA+mnist
- 基于python，利用主成分分析（PCA）和K近邻算法（KNN）在MNIST手写数据集上进行了分类。 经过PCA降维，最终的KNN在100维的特征空间实现了超过97%的分类精度。(Based on python, it uses principal component analysis (PCA) and K nearest neighbor algorithm (KNN) to classify on the MNIST handwritten data set. After PCA dime