搜索资源列表
复杂网络提取图像边缘特征
- 使用复杂网络提取图像边缘特征并进行识别的源代码，采用PCA_LDA算法对特征进行降维分类识别，识别效率很高。鲁棒性好
PCA_LDA.rar
- 《机器学习》课上的作业，PCA和LDA降维，尽管网上很多，但很少注释，另外细节上也没注意。这里有很详细的注释。另外还附上一个Naive贝叶斯分类器，大家可以作比较。附带的图像包是OLR人脸。ReducedDim为想要提取的特征数，不是百分比！," Machine learning" classes on the homework, PCA and LDA dimensionality reduction, even though a lot of online, but f
PCAPSVM
- PCA+SVM，对图像进行降维分类，并在yale库上测试取得比较好的效果-PCA+ SVM, dimensionality reduction of image classification, and yale library to achieve better test results
Rec_BaseOnCN
- 使用复杂网络提取图像边缘特征并进行识别的源代码，采用PCA_LDA算法对特征进行降维分类识别，识别效率很高。鲁棒性好-Extracted using image edge characteristics of complex networks and to identify the source code, using PCA_LDA algorithm to reduce the dimensions feature classification, identification with hi
pattern-recognition-simulation
- 用mushrooms数据对模式识别课程讲述的各种模式分类方法[线性分类，Bayesian分类，Parzen窗，KNN]和特征选择和降维方法[PCA,LDA]进行了模拟，并给出了各类分类方法的结果，-It s the simulations about linear classification ,Bayesian ,Parzen and KNN of pattern recognition .And ,It gives the results.
PCA
- 对输入的高维特征向量进行pca降维后输出低维的特征向量-PCA dimensionality reduction
mddm
- MDDM是一种多类标分类降维算法，使用成对约束对正类和负类进行迭代- is a novel algorithm to deal with the multilabel dimensionality reduction.
renlianshibie
- 利用PCA对人脸图像进行降维，然后训练神经网络分类器的Matlab程序-the Matlab in face recoginization using PCA algorithm
pcasearch
- 基于焊接图片的pca降维，knn分类算法。-Pca-based solder image dimension reduction, knn classification algorithm.
fsvmPpca-face-Recognition
- 首先用PCA对ORA人脸图像降维，然后用模糊支持向量机对提取的特征向量进行分类，识别率较高。-First using PCA for dimensionality reduction ORA face image, and then use fuzzy support vector machine to classify the extracted feature vectors, the recognition rate is higher.
PCA
- 模式识别作业-完全自编仿真程序。先用PCA对IRIS数据集进行降维，然后用最小错误法对降维的数据进行分类。压缩包中既包括matlab源代码，又有自己写的报告，还有.MAT格式的IRIS数据集用作程序调用。程序有详细注释，很容易懂。最后结果输出到txt文件中。-Pattern recognition operations- completely self simulation program. First on the IRIS data set with PCA dimension reduct
ensembles_pca_svm_new10v
- pca做特征降维，然后进行特征空间随机分割构造多个svm分类器，并行处理，对样本进行分类，基于特征空间的svm多分类器-using pca reduce feature dimension,split feature space and then randomly divided over svm classifier construction, parallel processing, the samples were classified, based on multi-feature sp
SPP-master
- 稀疏投影保持降维算法，用于高维度数据降维分类和回归的算法-Projections remain sparse dimension reduction algorithm for high-dimensional data dimensionality reduction classification and regression algorithm
cplst
- 多标签分类算法，通过对标签降维（SVD），然后利用线性回归建立特征和低维标签之间的关系，求出特征的系数，然后反过来进行预测(Multi label classification algorithm, through the tag dimension reduction (SVD), and then use linear regression to establish the relationship between features and low dimensional tags, to
example_knn(降维)
- knn降维算法用于图像分类，将所给样本遥感图像按照要求进行分类(KNN dimensionality reduction algorithm for image classification)
PCA
- 采用INP数据（145*145*200），该数据有16个类别， PCA进行数据降维，然后对降维数据采用kNN分类（k=1）。(Using INP data (145*145*200), the data has 16 categories, PCA carries out data reduction, and then uses kNN classification for dimensionality reduction data (k=1).)
lda-0.2-matlab.tar
- lda降维方法源码，lda 方法有很牛逼的降维效果，对分类精度有很大的提升(lda dimension reduction method source code, lda method is very Niubi dimension reduction effect, classification accuracy has greatly improved)
第一次作业_基于分类算法的雷达状态识别
- 第一次作业_基于分类算法的雷达状态识别 对于本数据集中的雷达状态识别，数据降维前使用朴素贝叶斯、支持向量机、神经网络的分类算法对于识别的准确率无太大影响；数据降维后使用神经网络算法最优，支持向量机算法其次，朴素贝叶斯算法较差。此外，训练样本越多，分类准确率有小幅度提高。(First Operation Radar State Recognition Based on Classification Algorithms For radar state recognition
PCA+SVM
- 先用PCA降维，在利用支持向量机进行分类，这个分类是二分类，所以PCA的降维降到两维即可分类。(Firstly, PCA dimensionality reduction is used to conduct classification with support vector machine. This classification is binary classification, so the dimensionality reduction of PCA can be reduced t
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