搜索资源列表
ID3
- ID3算法的C++实现，实现通过训练集建立决策树，测试集可以测试决策树的准确性-the realize of ID3 algorithm by c++
TextonBoostSplits
- Textonboost用boosting实现基于纹理特征的图像分类，里面有训练集、测试集和验证集，具有一定参考价值。-Textonboost uses boosting to realize image classification based on texture features, which has training set, test set and validation set, which has a certain reference value.
Linear-learner
- 基于PCA的线性学习器的分类方法，含完整Matlab程序及训练测试集，用于人脸识别。-Linear learner
KNN_Classifier
- 用MATLAB实现的分类器，算法为KNN，分类效果较好。文件中提供了相关的测试集。-MATLAB classifier, the algorithm for KNN, classification effect is better File to provide the related test set
K_NearestNeighbor
- matlab K-临近算法分类，对两组数据分成样本集和测试集进行分类判别，最后得出准确率 K-NearestNeighbor K-临近算法 dataA 数据集A dataB 数据集B pca 是否进行pca降维(0 or 1) metric 距离类型(pdist2) K -K- approaching classification algorithm, data is divided into two groups of samples and test se
SVM-class
- 这是关于svm的java源代码，带训练集，和测试集-This is about svm java source code, with training set and test set
Collaborative-Filtering
- u1.base和u1.test为训练集和测试集，分别来自MovieLens数据集， 本程序只是很简单的基于用户的协同过滤算法 运行算法所需要的配置信息，包括读取训练集和测试集还有最近邻个数的选择都在Base.java文件中可以找到 本程序的主程序是Application.java 仅供参考，希望对大家有帮助-Collaborative Filtering
feret
- feret人脸识别数据库,用于人脸分类训练，分为训练集合测试集-feret face recognition for human face classification training, divided into a training set of test set
elmtrain
- 将整个数据集中的103个样本随机划分为训练集与测试集，其中训练集包含80个样本，测 试集包含23个样本； 建立极限学习机模型，并训练； 利用训练好的极限学习机模型对测试集中的23个样本进行预测； 输出结果并绘图（真实值与预测值对比图）； -The 103 random samples of the entire data set is divided into training set and test set, wherein the training s
biaoqingshibie
- 是对jaffe人脸库进行识别测试的主程序，将jaffe人脸库分为训练集和测试集两部分，首先对图片进行LBP+LPQ特征提取，然后svm分类识别，统计识别率 -Is jaffe face recognition test the main library, the library will jaffe face divided into training and test sets of two parts, the first of LBP+LPQ image feature extrac
pcalda
- 基于pca和lca的人脸识别程序, 人脸库分为训练集和测试集两部分，统计识别率 -Based on pca face recognition program and lca, the face is divided into a training set and a test set of two parts, the recognition rate statistics
svm_
- SVM数据分类预测,选定训练集和测试集,相应的训练集的标签也要分离出来-SVM prediction data classification, the training set and test set is selected, the corresponding label should be separated the training set
Two-Variate-Function
- 使用BP神经网络实现二元函数的逼近问题，包含训练样本，无测试集-Using BP neural network to achieve the approximation of the two function function, including the training samples, no test set
bayes
- 首先对数据进行拆分，分为测试集与训练集，通过训练集进行贝叶斯网络的建模，最后利用建立的模型进行预测或分类任务的R语言代码-First, the data is split into a training set and test set, Bayesian network modeling through the training set, and finally the use of the model to predict or classify tasks R language code
facerecognize
- 根据pca主程序分析的人脸识别。测试集用于训练特征脸空间，测试集是一张人脸一张动物脸，程序目的是识别出人脸与非人脸-Face recognition based on the analysis of the main pca. Test set for training Eigenface space, a test set is a human face animal face, the program aims to identify the face and non-face
Autoencoder_Code
- 实现对于数据特征的识别和提取，进而实现重构，重构值与输入数据相差越小越好，对于该深度学习网络的训练可以分为预训练和调优过程，对此需要把数据按比例分为训练集和测试集，进行系数的调整，从而实现数据重构。-For realization identify and extract data features, thus achieving the reconstruction, reconstruction of the input data value difference as small as p
proj4
- 使用滑动窗的人脸检测，滑动窗口能够独立地对图片块进行分类，以确定是否属于被检测目标。内容如下： 1)载入正样本训练集（人脸），并将其转化为HoG特征 2)载入负样本训练集（没有人脸的任意场景），也将其转化为HoG特征 3)使用SVM，对分类器进行训练，训练集包括正训练集和负训练集 4)使用训练好的分类器，在不同的尺度上，对测试集进行分类 -Face detection with a sliding window.
digits
- 手写体二进制图片库，用于机器学习的训练集和测试集。-Handwriting binary image gallery, used for training set and testing set of machine learning.
email
- 机器学习算法的数据集，包含训练集和测试集。主要用于邮件分类-Machine learning algorithms of data sets, including training set and testing set.Mainly used for E-mail classification
444
- 算法流程：选定训练集和测试集-数据预处理-交叉验证选择最佳参数-分类准确率-预测-利用最佳参数训练SVM-Algorithm flow: selected training set and test set- data preprocessing- cross-validation selection of the best parameters- classification accuracy- prediction- training SVM using the best parameter