搜索资源列表
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
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
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
NLPLibSVM
- libsvm分词训练集的java版本。包括libsvm.jar以及训练集样本-Libsvm version of the Java word segmentation training set. Including libsvm.jar and training set samples
Face-Recognition
- 本程序设计了一个基于PCA方法的人脸识别系统。该系统可以对各类文件格式的人脸文件进行分析,可以对一定长度的人脸训练集进行特征值提取,能够显示特征脸、平均脸,并且完成比对误差及人脸识别的功能。-This design program a face recognition system based on PCA method.The system can analyze the face of all kinds of file format file, you can face the train
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
Edit68CMU_pack
- 人脸对齐基于论文《fps3000》中训练集构建时生产68个特征点的matlab脚本文件。提供交互窗口,按顺序点左眼右眼和嘴巴,即可自动生成68个点,再用鼠标对个别点进行人工修正。满意后保存即生成一个文本文件,存有68个点的坐标。-Face aligned on the paper fps3000 production when the training set to build 68 feature points matlab scr ipt file. Provide interactiv
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.
demoadaboost
- Adaboost是一种迭代算法,其核心思想是针对同一个训练集训练不同的分类器(弱分类器),然后把这些弱分类器集合起来,构成一个更强的最终分类器(强分类器)。-Adaboost is an iterative algorithm, the core idea is the same for a training set different classifiers (weak classifiers), and then set up these weak classifiers to form a
FaceDetection
- 该程序实现功能:基于opencv的人脸检测 文件中包含人脸检测的训练集haarcascade_eye.xml和haarcascade_frontalface_alt2.xml-The program implements functions: Based on opencv face detection file contains face detection training set haarcascade_eye.xml and haarcascade_frontalface_alt2.xm
k_nn
- kNN的思想:计算待分类的数据点与训练集所有样本点,取距离最近的k个样本;统计这k个样本的类别数量;根据多数表决方案,取数量最多的那一类作为待测样本的类别。距离度量可采用Euclidean distance,Manhattan distance和cosine。-kNN The idea is simple: the training set and calculated data points to be classified all sample points taken the neare
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
num
- 基于pca数字识别,成功率达百分之九十五以上,内带有测试、训练集,可直接运行。-Based on pca digital identification, the success rate of more than 95 percent, with a test, training set, can be run directly.
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