What is Support in Support Vector Machines!? In SVMs the resulting separating hyper-plane is attributed to a sub-set of data feature vectors (i.e., the ones that their associated Lagrange multipliers are greater than 0). These feature vectors were named support vectors because intuitively you could say that they “support” the separating hyper-plane or you could say that for the separating hyper-plane the support vectors play the same role as the pillars to a building.
SVM algorithms use a set of mathematical functions that are defined as the kernel. The function of kernel is to take data as input and transform it into the required form. Different SVM algorithms use different types of kernel functions. These functions can be different types. For example linear, nonlinear, polynomial, radial basis function (RBF), and sigmoid.
Introduce Kernel functions for sequence data, graphs, text, images, as well as vectors. The most used type of kernel function is RBF. Because it has localized and finite response along the entire x-axis.
The kernel functions return the inner product between two points in a suitable feature space. Thus by defining a notion of similarity, with little computational cost even in very high-dimensional spaces.
- It is really effective in the higher dimension.
- Effective when the number of features are more than training examples.
- Best algorithm when classes are separable
- The hyperplane is affected by only the support vectors thus outliers have less impact.
- SVM is suited for extreme case binary classification.
- For larger dataset, it requires a large amount of time to process.
- Does not perform well in case of overlapped classes.
- Selecting, appropriately hyperparameters of the SVM that will allow for sufficient generalization performance.
- Selecting the appropriate kernel function can be tricky.
Originally published at https://www.jayvinay.com on September 2, 2020.
