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Train Support Vector Machines Using Classification Learner App
This example shows how to construct support vector machine (SVM) classifiers in the Classification Learner app, using the
ionosphere
data set that contains two classes. You can use a support vector machine (SVM) with two or more classes in Classification Learner. An SVM classifies data by finding the best hyperplane that separates all data points of one class from those of another class. In the ionosphere
data, the response variable is categorical with two levels: g
represents good radar returns, and b
represents bad radar returns.- In MATLAB®, load the
ionosphere
data set and define some variables from the data set to use for a classification. - On the Apps tab, in the Machine Learning group, click Classification Learner.
- On the Classification Learner tab, in the File section, click New Session.In the New Session dialog box, observe that the app has selected response and predictor variables based on their data type. The response variable
Group
has two levels. All the other variables are predictors. - Click .Classification Learner creates a scatter plot of the data.
- Use the scatter plot to visualize which variables are useful for predicting the response. Select different variables in the X- and Y-axis controls. Observe which variables separate the class colors most clearly.
- To create a selection of SVM models, on the Classification Learner tab, in the Model Type section, click the down arrow to expand the list of classifiers, and under Support Vector Machines, click .Then click .
Tip
If you have Parallel Computing Toolbox™ then the first time you click you see a dialog while the app opens a parallel pool of workers. After the pool opens, you can train multiple classifiers at once and continue working.Classification Learner trains one of each nonoptimizable SVM classification option in the gallery, and highlights the best score. The app outlines in a box the Accuracy score of the best model. - Select a model in the History list to view the results. Examine the scatter plot for the trained model and try plotting different predictors. Misclassified points are shown as an X.
- To inspect the accuracy of the predictions in each class, on the Classification Learner tab, in the Plots section, click Confusion Matrix. View the matrix of true class and predicted class results.
- Select the other models in the list to compare.
- Choose the best model in the History list (the best score is highlighted in a box). To improve the model, try including different features in the model. See if you can improve the model by removing features with low predictive power.Then it will restart and will be unlocked after a few minutes have passed.Tool 2. Samsung galaxy s5 t mobile unlock code free.On the Classification Learner tab, in the Features section, click . In the Feature Selection dialog box, specify predictors to remove from the model, and click to train a new model using the new options. Compare results among the classifiers in the History list.
- To investigate features to include or exclude, use the parallel coordinates plot. On the Classification Learner tab, in the Plots section, select Parallel Coordinates Plot.
- Choose the best model in the History list. To try to improve the model further, try changing SVM settings. On the Classification Learner tab, in the Model Type section, click . Try changing a setting, then train the new model by clicking . For information on settings, see Support Vector Machines.
- To export the trained model to the workspace, select the Classification Learner tab and click . See Export Classification Model to Predict New Data.
- To examine the code for training this classifier, click . For SVM models, see also Generate C Code for Prediction.
Use the same workflow to evaluate and compare the other classifier types you can train in Classification Learner.
To try all the nonoptimizable classifier model presets available for your data set:
- Click the arrow on the far right of the Model Type section to expand the list of classifiers.
- Click , then click Train.
To learn about other classifier types, see Train Classification Models in Classification Learner App.
Related Topics
Example code for how to write a SVM classifier in MATLAB.
How to Run:
To run the code, create two directories to store two categorical sets of image data. These directories of images will be used to train an SVM classifier.
Then, set the two variables in main_script, image_set_directory and image_set_complement_directory,equal to the directory paths where the training images are currently being stored. Finally run the main script to generate an SVM classifier data structure. The SVM classifier data structure can then be used to determine what category an unclassified image best fits.
Svm Matlab Code Free Download Full
The default configuration of the main_script.m file is two create a SVM classifier to make a classification decision of whether an unclassifed image best fits within a set of flower images, or set of foliage images. The script then proceeds to test how well the generated SVM classifier works by classifying a set unlabeled images and comparing its results to whether the image content is actually a picture of flowers or foliage.
The main_script can be changed to skip the testing of the SVM classifier and just return the SVM data structure needed for image classification.
Basic Principle:
Matlab Svm Regression
The code works using the Support Vector Machine (SVM) classification algorithm (see en.wikipedia.org/wiki/Support_vector_machine for more information). It is important to keep in mind that an SVM is only capable of making a binary classifiaction. In other words, an SVM can only be trained to differentiate between two categories of training data at a time. Therefore, differentiating between more than two categories at a time is beyond the scope of this program.
The SVM in this code is used classify sets of images. To do this, a set of general statisics is generated by finding the corner points in an image and calculating the average and standard deviation of the pixel intesities around the cornor points.