搜索资源列表
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++
character-training-set
- 车牌识别,各省汉字训练集,全是手动筛选的,部分省市素材缺少所以缺乏样本-License plate recognition, Chinese character training set
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.
plot_isotonic_regression
- 保序回归是寻找使训练集均方差最小的近似函数,它的优点是目标函数不要线性的。-The isotonic regression finds a non-decreasing approximation of a function while minimizing the mean squared error on the training data. The benefit of such a model is that it does not assume any form for the tar
BP_Classifier
- 用MATLAB实现的简单分类器,算法为BP神经网络,为监督学习,需要训练集(文件中附有训练集,供测试用),分类效果较好。-This program creates a Classifier to identify the gender by height and weight based on BP network.
syn_13
- 以网格采样方法构建训练集,训练决策树,对图像分类。-Grid sampling method for constructing the training set, training the decision tree, for image classification.
syn10_1
- 以多边形采样结果构建训练集,对图像进行分类-Polygon sampling results build the training set, the image classification
ColorIndex
- 在Corel 5k数据库中,首先提取训练集和测试集中所有图像的直方图信息(AllHist.m),然后利用直方图相交法检索图像(ColorIndex.m)。-Firstly, read all images, and get their hists, then retrieve image by color index.
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
fisher_classify
- MATLAB版本的LDA线性分类器,具体包括计算类内离散度矩阵,类间离散度矩阵,以及训练集各类在新坐标轴上的投影。代码原来用于肌电特征的分类,亦可用于其他机器学习案例-the LDA classifier wrote in MATLAB
NN_tutorial
- 基于NN网络的图像训练及分类,程序中包含输入图像矩阵,训练后测试具有100 正确率,可以自己加入不同的训练集,直接运行即可-NN network-based image classification training and procedures contained in the input image matrix test after training with 100 accuracy, can add their own different training set can be ru
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
BPtrain
- BP神经网络实现测试数据预测(将训练集与测试集数据进行归一化 建立BP神经网络,并训练;利用训练好的BP神经网络对测试集中的23个样本的抗压强度进行预测;输出结果并绘图)-BP neural network to predict the test data (the training set and test data set is normalized the BP neural network and training use of the trained BP neural netwo
GRNN_PNN
- 将训练集与测试集数据进行归一化; 建立GRNN或PNN神经网络; 利用建立好的神经网络对测试集中的26个乳腺组织样本的类型进行预测; 计算预测正确率(不必计算每类的正确率,只需计算正常或者病变两类的正确率,即只要预测结果与真实值属于同一大类,则认为是正确,否则认为预测错误)-The training set and test data set is normalized Establish GRNN or PNN neural network The use of wel
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
lssvmtest
- PSO-LSSVM灰色组合模型代码,包括对训练集的预处理、参数寻优、模型确定,实现分类-PSO-LSSVM gray combined model code, including the pre-treatment of the training set, parameter optimization, the model is determined to achieve classification