搜索资源列表
Recognition
- 将数量较少的故障样本分为训练集和测试集,实现故障的分类和识别(A small number of fault samples are divided into training set and test set to realize fault classification and recognition.)
svc
- 利用支持向量机方法对不同类别的对象进行分类(use the SVM method to classify)
LibSVM1.0.10
- weka3.7 LibSVM.jar,主要用于weka进行SVM分类时,找不到jar包,报错LibSVM不在classpath(weka3.7,LibSVM.jar,When SVM is used for classification in weka, jar package is not found, and LibSVM is not in classpath.)
svm计算程序
- 快速svm线性核计算,可以用于分类,经典的分类模型,可以学习一下
SVM_GUI_3.1
- 支持向量机实现分类识别,带有图形用户界面,有测试数据以及具体使用说明。(Support vector machine realizes classification and recognition, with graphical user interface, test data and specific instructions.)
Canupo
- C++编码,基于线性判别分析(LDA)和支持向量机(SVM)的多尺度维度特征点云分类算法,通过机器学习方法精确分类。效果可达95%以上,本文件夹内含有详细中文教程。
wine
- SVM多分类算法,基于svmlib适合初学者学习(SVM multi classification algorithm, based on svmlib suitable for beginners to learn)
hog-feature
- 方向梯度直方图(Histogram of Oriented Gradient, HOG)特征是一种在计算机视觉和图像处理中用来进行物体检测的特征描述子。它通过计算和统计图像局部区域的梯度方向直方图来构成特征。Hog特征结合SVM分类器已经被广泛应用于图像识别中,尤其在行人检测中获得了极大的成功。需要提醒的是,HOG+SVM进行行人检测的方法是法国研究人员Dalal在2005的CVPR上提出的,而如今虽然有很多行人检测算法不断提出,但基本都是以HOG+SVM的思路为主(The Histogram
HOGSVM
- 输入训练样本集和测试样本集,通过提取HOG然后用SVM实现分类。(Input training samples and test samples, extract HOG and implement classification with SVM.)
6.代码
- 实现分类,回归的算法,可以直接下载运行验证(Implementation of classification, regression algorithm, can be directly downloaded operation verification)
SVM
- 调用于sklearn平台的支持向量机算法,有着较好的分类能力(The support vector machine algorithm for sklearn platform has good classification ability)
SVM
- 经典的分类器,在很多样本数据上表现优良,是最好的单分类器。(The classical classifier performs well on many sample data and is the best single classifier.)
GASVM
- 遗传算法优化支持向量机程序,用于参数寻优,提高分类率(Genetic algorithm optimization support vector machine program)
fenlei
- 利用hog提取特征输入到svm分类器中,适用于新手(Using hog extraction feature input to svm classifier, suitable for novices)
MachineLearningLab-master
- 使用2种分类方法随机森林、SVM对数据进行分类(Classification of data using random forests and SVM)
20171211留档
- 利用SVM对制备的样本进行三分类,对图像进行三角形匹配,模板匹配(SVM was used to classify the samples in three categories. Triangle matching and template matching were applied to the images.)
libSVM
- 利用LibSVM以及SVM模型进行文本数据分类。训练数据与测试数据都有。(Using LibSVM and SVM model to classify text data. Both the training data and the test data are available.)
Sample4
- 支持svm多分类,运算时间较长,支持svm多分类的matlab代码,精度不高。(Support svm multi-classification)
SVM
- 语音情感识别分类,在中科大录制的语音情感数据库CASIA中来实现的(Speech emotion recognition and classification is implemented in CASIA, a speech emotion database recorded by China University of science and technology.)
SVM_python
- 读取地震数据并进行SVM训练分类,针对特殊数据进行训练。输入:坐标,输出:标签(Read seismic data and conduct SVM training classification, training for special data. Input: coordinate, output: Label)