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  • 高维多标签分类matlab

    2020-12-30 15:19:01
    高维多标签分类matlab knn,svm,随机森林等算法 784维数据 分为10类
  • 使用matlab处理分类数据多标签问题

    千次阅读 2019-01-20 22:33:40
    在使用matlab处理数据标签时, 1,首先要判断这张图片标签是否是符合规则的,对于不符合要舍去。在某些情况下,可能不能完全舍去。那么就需要,根据保留标签的特点,来存储数据。比如 使用case 语句。 2,标签可以...

    在使用matlab处理数据标签时,

    1,首先要判断这张图片标签是否是符合规则的,对于不符合要舍去。在某些情况下,可能不能完全舍去。那么就需要,根据保留标签的特点,来存储数据。比如 使用case 语句。

    2,标签可以过多或者需要融合,可以根据原始图片的特点来融合数据。

     

    展开全文
  • matlab自带分类工具分类箱使用

    千次阅读 2019-07-07 21:34:40
    其中本人matlab为2016b,工作空间中Data为540行12列,540个样本,11个维度,最后一列为标签。 打开maltab软件,输入“classificationLearner”,回车。 输入数据,选择“From Workspace”. 因为我的样本是行排列,则...

    采用MATLAB,对数据进行自带工具箱的使用练习

    准备:matlab,Data。其中本人matlab为2016b,工作空间中Data为540行12列,540个样本,11个维度,最后一列为标签。

    1. 打开maltab软件,输入“classificationLearner”,回车。
    2. 输入数据,选择“From Workspace”.在这里插入图片描述
    3. 因为我的样本是行排列,则选择“use columns as variables ”,最后一行数据选择Response。右栏为校验选择。选择好后选择“start session”.数据
    4. 点图为某两个维度之间的显示,可以选择右侧进行观察多个。工具栏选择ALL,注意“use parallel”不选。点击“train”.将得到多个分类器的分类准确度结果,其中最高的会被自动标注。数据分类
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  • matlab----标签图像分类训练工具

    千次阅读 2017-12-04 18:18:36
    Label Images for Classification Model...matlab—-标签图像分类训练工具 http://cn.mathworks.com/help/vision/ug/label-images-for-classification-model-training.html?s_tid=srchtitle Description The Trainin

    Label Images for Classification Model Training

    matlab—-标签图像分类训练工具

    http://cn.mathworks.com/help/vision/ug/label-images-for-classification-model-training.html?s_tid=srchtitle

    Description

    The Training Image Labeler provides an easy way to label positive samples that the trainCascadeObjectDetector function uses to create a cascade classifier. Using this app, you can:

    1. Interactively specify rectangular regions of interest (ROIs).

    交互指定感兴趣的举行区域

    1. Using the ROIs, you can detect objects of interest in target images with the vision.CascadeObjectDetector System object.

      借助于ROI用户可以使用`vision.CascadeObjectDetector对象检测目标图像中的感兴趣对象。

    2. You can load multiple images at one time, draw ROIs, and then export the ROI information in the appropriate format for the trainCascadeObjectDetector. The labeler app supports all image data formats that the trainCascadeObjectDetector function uses.

      用户可以一次加载多幅图片,画出ROI并能够以适当的方式为trainCascadeObjectDetector导出ROI信息。

    Open the Training Image Labeler

    1. MATLAB® Toolstrip: Open the Apps tab, under Image Processing and Computer Vision, click the app icon.
      打开App工具栏,在Image Processing and Computer Vision子项目下,打开trainingImageLabeler程序。如下图所示:

      打开如下图所示:
    2. MATLAB command prompt: Enter trainingImageLabeler.
      使用命令trainingImageLabeler打开工具。

    App Controls

    You can add an unlimited number of images to the Data Browser. You can then select, remove, and create ROIs, and save your session. When you are done, you can export the ROI information to an XML file.
    用户可以想Data Browser中添加图片,并且图片的数量没有限制。用户可以选择、移除和创建用户感兴趣区域(ROI),并且可以保存用户会话。当用户完成以上工作时,用户可以将ROI信息导出为XML文件。

    Add Images

    Use the Add Images icon to select and add images to the Data Browser. You can add more images at any time during your editing session. The source images remain in the folder where they were originally located. The app does not make copies of the original images or move them. If you rotate the images in the Data Browser, the app overwrites the images in their original location with the modified orientation.

    The app provides a list of image thumbnails that you loaded for the session. Next to each thumbnail, you see the file name and number of ROIs created in that image.

    Specify Regions of Interest

    After you load images, you can delineate ROIs. You can switch between images and continue creating ROIs. Drag the cursor over the object in the image that you want to identify for an ROI. You can modify the size of an ROI by clicking either the corner or side grips. To copy and paste an ROI, left-click within it’s border to select it. You can select one or more ROIs to move or to copy and paste. To delete an ROI, click the red x-box,, in the upper-right corner.

    You can also use the following shortcuts:

    1. Control-C to copy
    2. Control-V to paste
    3. Control-X to cut

    Remove, Rotate, and Sort Images

    You can remove, rotate, or sort the images. Right-click any image to access these options. To select multiple images, press Ctrl+click. To select consecutive images, press Shift+click. To sort images by the number of ROIs, from least amount of ROIs contained in each image, right-click any image and select Sort list by number of ROIs.

    New Session

    When you start a new session, you can save the current session before clearing it.

    Open Session

    You can open a new session to replace or add to the current session. The app loads the selected .MAT session file. To replace your current session, from the Open Session options, select Open an existing session. To combine multiple sessions, select Add session to the current session.

    Save Session

    You can name and save your session to a .MAT file. The default name is LabelingSession. The saved session file contains all the required information to reload the session in the state that you saved it. It contains the paths to the original images, the coordinates for the ROI bounding boxes for each image, file names, and logical information to record the state of the files.

    Export ROIs

    When you click the Export ROIs button, the app exports the ROI information to the MATLAB workspace in a 1-by-M structure, where M represents the number of images. The structure contains two fields. One field stores the image file location and the other field stores the corresponding ROI information for each image. You are prompted to name the variable or to accept the default positiveInstances name. The first field, imageFileName, contains the full path and file name of the images. The app does not copy and resave images, so the stored path refers to the original image and folder that you loaded the images from. The second field, objectBoundingBoxes, contains the ROI [x, y, width, height] information.

    展开全文
  • 内含有大量多标签多类别分类算法和对应的代码,包括MIML_LPT,MIMLBoost,MIMLSVM,MIMLfast,KISAR,MIMLKNN,MLKNN,DMIMLSVM,MIMLMISVM等等,部分代码含有对应的文献,学习多类标分类的好资源。
  • Matlab分类准确率代码

    2018-01-12 14:43:50
    matlab实现的计算但标签分类聚类准确率的计算代码。函数包含两个参数groundtruth以及分类/聚类模型给出的预测标签向量
  • Matlab分类器大全

    千次阅读 2016-01-12 20:51:25
    train_data是训练特征数据, train_label是分类标签。 Predict_label是预测的标签MatLab训练数据, 得到语义标签向量 Scores(概率输出)。1.逻辑回归(多项式MultiNomial logistic Regression)Factor = mnrfit...

    train_data是训练特征数据, train_label是分类标签。
    Predict_label是预测的标签。
    MatLab训练数据, 得到语义标签向量 Scores(概率输出)。

    1.逻辑回归(多项式MultiNomial logistic Regression)

    Factor = mnrfit(train_data, train_label);
    Scores = mnrval(Factor, test_data);

    scores是语义向量(概率输出)。对高维特征,吃不消。

    2.随机森林分类器(Random Forest)

    Factor = TreeBagger(nTree, train_data, train_label);
    [Predict_label,Scores] = predict(Factor, test_data);

    scores是语义向量(概率输出)。实验中nTree = 500。
    效果好,但是有点慢。2500行数据,耗时400秒。500万行大数据分析,会咋样?准备好一篇小说慢慢阅读吧^_^

    3.朴素贝叶斯分类(Naive Bayes)

    Factor = NaiveBayes.fit(train_data, train_label);
    Scores = posterior(Factor, test_data);
    [Scores,Predict_label] = posterior(Factor, test_data);
    Predict_label = predict(Factor, test_data);
    accuracy = length(find(predict_label == test_label))/length(test_label)*100;

    效果不佳。

    4.支持向量机SVM分类

    Factor = svmtrain(train_data, train_label);
    predict_label = svmclassify(Factor, test_data);

    不能有语义向量 Scores(概率输出)

    支持向量机SVM(Libsvm)

    Factor = svmtrain(train_label, train_data, '-b 1');
    [predicted_label, accuracy, Scores] = svmpredict(test_label, test_data, Factor, '-b 1');

    5.K近邻分类器 (KNN)

    predict_label = knnclassify(test_data, train_data,train_label, num_neighbors);
    accuracy = length(find(predict_label == test_label))/length(test_label)*100;

    不能有语义向量 Scores(概率输出)

    IDX = knnsearch(train_data, test_data);
    IDX = knnsearch(train_data, test_data, 'K', num_neighbors);
    [IDX, Dist] = knnsearch(train_data, test_data, 'K', num_neighbors);

    IDX是近邻样本的下标集合,Dist是距离集合。
    自己编写, 实现概率输出 Scores(概率输出)

    Matlab 2012新版本:

    Factor = ClassificationKNN.fit(train_data, train_label, 'NumNeighbors', num_neighbors);
    predict_label = predict(Factor, test_data);
    [predict_label, Scores] = predict(Factor, test_data);

    6.集成学习器(Ensembles for Boosting, Bagging, or Random Subspace)
    Matlab 2012新版本:

    Factor = fitensemble(train_data, train_label, 'AdaBoostM2', 100, 'tree');
    Factor = fitensemble(train_data, train_label, 'AdaBoostM2', 100, 'tree', 'type', 'classification');
    Factor = fitensemble(train_data, train_label, 'Subspace', 50, 'KNN');
    predict_label = predict(Factor, test_data);
    [predict_label, Scores] = predict(Factor, test_data);

    效果比预期差了很多。不佳。

    7.判别分析分类器(discriminant analysis classifier)

    Factor = ClassificationDiscriminant.fit(train_data, train_label);
    Factor = ClassificationDiscriminant.fit(train_data, train_label, 'discrimType', '判别类型:伪线性...');
    predict_label = predict(Factor, test_data);
    [predict_label, Scores] = predict(Factor, test_data);

    转自:http://blog.csdn.net/abcjennifer/article/details/17137785

    展开全文
  • 转自文章MatLab2012b/MatLab2013b分类器大全(svm,knn,随机森林等) train_data是训练特征数据, train_label是分类标签。 Predict_label是预测的标签MatLab训练数据, 得到语义标签向量 Scores(概率输出)。 ...
  • MatLab2012b/MatLab2013b 分类器大全(svm,knn,随机森林等)

    万次阅读 多人点赞 2013-12-05 10:50:30
    train_data是训练特征数据, train_label是分类标签。Predict_label是预测的标签MatLab训练数据, 得到语义标签向量 Scores(概率输出)。1.逻辑回归(多项式MultiNomial logistic Regression)Factor = mnrfit(train_...
  • %% 本函数的功能是根据所给分类方程,产生一组带标签的数据点(二维) % row表示需要的数据点个数; % range_x、range_y分别是数据[横纵坐标取值范围],都是 1*2 矩阵. % 分类方程形式:y = a*x^m + b function data = ...
  • matlab分类器函数

    千次阅读 2017-02-16 10:47:53
    train_data是训练特征数据, train_label是分类标签。 Predict_label是预测的标签MatLab训练数据, 得到语义标签向量 Scores(概率输出)。 1.逻辑回归(多项式MultiNomial logistic Regression) Factor = ...
  • %% 生成txt格式的图像分类标签 path='D:\人工智能\图像识别数据\'; folders=dir(path);% dir是direcory的缩写,是目录的意思,返回的folders是结构体,包含所有的目录和两个特殊的目录,一个是“.”,代表该文件夹...
  • matlab 自带分类器(2012b以上)

    千次阅读 2017-11-20 15:14:42
    train_data是训练特征数据, train_label是分类标签。 Predict_label是预测的标签MatLab训练数据, 得到语义标签向量 Scores(概率输出)。1.逻辑回归(多项式MultiNomial logistic Regression)Factor = mnrfit...
  • KNN基本思路KNN学习(K-Nearest Neighbor algorithm,K最邻近方法 )是一种非常简单的分类器,其基本思想是:输入没有标签(标注数据的类别),即没有经过分类的新数据,首先提取新数据的特征并与测试集中的每一个数据...
  • MATLAB中的分类

    2017-05-18 15:34:13
    MATLAB中的分类MATLAB分类器有:K近邻分类器,随机森林分类器,朴素贝叶斯,集成学习方法,鉴别分析分类器,支持向量机。现将其主要函数使用方法总结如下,更多细节需参考MATLAB 帮助文件。 设  ...

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