搜索资源列表
PG_BOW_DEMO-master
- 图像检索算法 BOW 有机器学习的部分 分为训练集和测试集 效果不错 不懂的可以阅读里面的readme.txt 文件
SVM.R
- 利用R语言实现SVM.已经编写为函数，只需输入测试集和训练集数据即可-using R to do SVM classify。user only have to upload training and test data
pmf
- 推荐系统 概率矩阵分解代码 内部已包含有数据集，已分为训练集与测试集-Recommended system probability matrix decomposition Code
emial-spam
- 基于感知器算法的垃圾邮件识别，先通过训练集训练出分类器，然后通过测试集验证-Perceptron based spam detection algorithm
self-taught-learning
- 自主学习把稀疏自编码器和分类器实现结合。先通过稀疏自编码对无标签的5-9的手写体进行训练得到最优参数，然后通过前向传播，得到训练集和测试集的特征，通过0-4有标签训练集训练出softmax模型，然后输入测试集到分类模型实现分类。-Independent Learning the encoder and the sparse classifiers achieve the combination. First through sparse coding since no label was ha
kennard_stone
- Kennard Stone 算法 用于数据集的划分（训练集 和 测试集） 算法同时输出训练集、测试集，以及训练集或测试集中样品在原数据集中的编号信息，方便样本的查找。 原始代码来源于本网，自行重新编译，如有需要，欢迎下载。 -Kennard Stone agorithm for the partition of data set (training set and test set) the outputs of the agorithm not only include
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
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