Support Vector Machine

Um, what am i looking at?

This is a visualization of Support Vector Machine, which tries to fit the best possible decision Boundary from the given data points.

Just tap on the grid(Data Space) to add a new data points ( you can also drag the data points by click and drag), if you want to change the class of a new data point then simply click on the ‘Current Class’ switch.

Go ahead, play with the controls on the right and have fun!

From Wiki:-

In machine learning, support-vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on the side of the gap on which they fall.

Hyperparameters:

C: Regularization term

Kernel Type: Type of Kernel, currently support 3:

Linear:

[K(x,y) = x^Ty]

Polynomial:

[K(x,y) = (x^Ty + c)^d]

RBF:

[K(x,y) = exp(-\frac{

x - y

^2}{2\sigma^2})]

We also have Hyperparamters to control the behavior of our Kernels:

If we select Polynomial Kernel then we can choose the Degree of our polynomial upto 3.
If we select RBF Kernel then we can select the width of our kernel a.k.a sigma( \(\sigma\)).

Um, what am i looking at?

From Wiki:-

Hyperparameters:

Further Reading