搜索资源列表
Bayesian-based-classifier-design
- 基于贝叶斯的分类器设计.用“cancer.mat”的数据作为训练样本集，建立Bayes分类器，用测试样本数据对该分类器进行测试，从而加深对所学内容的理解和感性认识。-Based on the Bayes classifier. ' Cancer.mat data as the training sample set, the establishment of the Bayes classifier, the classifier is tested with the test sampl
logistic-regression
- 采用随机梯度算法，来利用逻辑回归对训练集进行分类-Classify the training set by logistic regression
haarcascade_profileface
- 在opencv2.4中找到了侧脸训练集，分享一下，供大家使用-opencv： face detection
MIT_persons_jpg
- MIT行人数据库，共924张行人图片，原来是PPM格式，很多人找不到转换工具，这里已转换成JPG。该数据库只含正面和背面两个视角，无负样本，未区分训练集和测试集。-MIT pedestrian dataset of JPG format, for pedestrian detection application.
Adaboost
- 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-Adaboost is an iterative algorithm, the core idea is the same training set for different classifiers (weak classifiers), and then set up these weak classifiers to form a stronger
Adaboost
- 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-Adaboost is an iterative algorithm, the core idea is the same training set for different classifiers (weak classifiers), and then set up these weak classifiers to form a stronger
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
pos
- 世界上名列前茅的人形数据集，用它训练的结果效果是有目共睹的。(person dataset used in classification training)
人头训练正负样本数据集
- 用来训练人头识别模型的正负样本数据集，正样本数据已经resize化。(The positive and negative sample data set is used to train the head recognition model, and the positive sample data has been resize.)