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SVM Demo

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An interactive demo of how an SVM works, with comparison to a perceptron

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SVMs are a bit tricky. In this case, we show a linear SVM and illustrate its behaviour on some 2D data. This should be great for getting to grips with maximising geometric margins, support vectors, and the optimisation involved in computing an optimal separating hyperplane.

Data can be generated randomly (uniformly or from separate gaussians) over the 2D space, and an SVM or perceptron can be trained to find a separating line. Data points can be dragged around with the mouse, and the model (perceptron or SVM) will retrain in real-time as the point is dragged (observe that dragging non-support vector points will not change the SVM decision boundary). The weights/bias terms can also be adjusted by dragging (either the weight vector arrow to change weights, or the decision boundary to change the bias); it should be clear that no configuration of the weights will give a larger minimum margin than that computed by an SVM. The weights/bias can also be randomised to illustrate the effect of random initial weights on the converged solution to the perceptron algorithm.

The program requires some implementation of a QP solver or SVM algorithm. Therefore, you will need to have one of:
- The bioinformatics toolbox, which includes an svmtrain function
- The optimization toolbox, which includes a quadprog function
- The third-party library "libsvm", which includes an svmtrain function.
If one or more of these is in the matlab path, the program should just work. To add a custom SVM solution, refer to the code commentary in LinearClassifier.
Code is extensively commented and documented. There are a number of outrageously obfuscated uses of arrayfun that may be of interest to people who enjoy incomprehensible code. The user interface code doesn't follow the preferred design pattern for Matlab GUI code because I didn't know of one when I wrote this; hence, please don't refer to the GUI code as a template for a pleasant and sensible Matlab GUIing experience.

Comments and Ratings (14)

azrin mizie

Hello sir,
i am currently doing my final year project to for braille character recognition and i would like to use your code as my reference..
i am using matlab R2016a and this is my email
thank you sir

renu gowtham

hi sir, how can i use this demo code in my project.

Sumit Patil

Ankur Rai

Dear Richard,
I have to classify a medical image in ROI(Region of Interest) and RONI(Region of Non Interest) for image watermarking, but able to create predictor data or features for SVM.
Please help me sir, i need your help.

Nur Sakinah

how to use this demo sir? I need a help and i am using matllab R2014b.

This is my email
It will be helpful if you can help me sir. Thankyou.

Chunghee Kim

Hello I was wondering why it doesn't work well with matlab 2015? I have the academic license. Does that make a difference?

godwin tgn


Tulips (view profile)

Hi Honza,
if you are referring to the case where data is not linearly separable, then it is true that the computation of the boundary and support vectors may seem strange.

Typically, we would construct an SVM with some tolerance for misclassified data; for example, we could compute a 'soft margin' (See Vapnik and Cortes, 1995), which optimises a trade-off between the maximum margin decision boundary and a small penalty for misclassification. In my program, I have set the misclassification penalty to be very large - since this can affect the position of the decision boundary even when data is linearly separable. I do this so that you can see the optimal separating hyperplane.

Obviously this is not representative of how you would solve a real non linearly separable problem - if you suspect that data is noisy, it would be appropriate to change the 'C' parameter in a soft-margin SVM. Alternatively, if you suspect that data is complex and non-linear, it may be better to use a non-linear kernel (e.g. rbf).

Regardless of where data is linear and/or separable, you should find that some data points that are correctly classified will be support vectors. The support vectors correspond to the data points that actually define where the decision boundary is. They comprise any misclassified datapoints (which will correspond to penalty terms in the soft-margin problem), and all the correctly classified datapoints which lie exactly the minimum distance away from the margin.

I hope this answers your question.


Honza (view profile)

Thanks for the demo. However, I'm not sure that the margin is estimated correctly in the non-linear case. Is it allright that correctly classified points (on the right side of the margin) act as support vectors? Thanks for clarification

Alex Frid

Mr Smart


Samad (view profile)



Added a graphics mode menu and an SVM algorithm menu.


Fixed a bug where the 'train' option was sometimes disabled inappropriately. Reduced the delay between perceptron epochs.


Updated for Matlab R2010a. Soft-margin constraints are now large-but-not-too-large so nobody gets upset when data is non-separable. Line smoothing/transparency removed. Bioinformatics svm training changed from LS to SMO.


Modified the auto-detection of SVM algorithm for additional easiness, and made the initial position of the window be decided based on screen size.


Added a link to the libSVM download page in the requirements section.

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