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Support Vector Machine
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Table of Content
Almost every learning job, including classification and numerical prediction, may be used with SVMs.
Statistical learning theory serves as an inspiration for SVM.
Various names Kernel techniques, Max-margin classifiers, and Large-margin classifiers
The SVM algorithm's job in a binary situation is to find a line dividing the two groups. A line, however, is unable to distinguish the classes in a multidimensional issue.
An SVM's objective is to construct a flat boundary known as a hyperplane that splits the space into homogenous sections.
There are several options for the dividing line that separates the groupings of circles and squares.
Maximum Margin Hyperplane (MMH) is sought for by SVM.
MMH is as far away from the convex hulls of the two groupings of data points as is physically possible.
The linear classifier with the greatest margin is known as the largest margin linear classifier. This SVM type, often known as an LSVM, is the most basic.
Non-Linear Spaces
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Kernel Tricks
The kernel trick, a technique used by SVMs, allows them to map the issue into a higher dimension space. A nonlinear connection may suddenly appear to be relatively linear once the kernel method has been done since we are viewing the data through a new dimension.
Kernel Functions
- The linear kernel does not transform the data at all. Therefore, it can be expressed simply as the dot product of the features:
- The sigmoid kernel results in an SVM model somewhat analogous to a neural network using a sigmoid activation function. The Greek letters kappa and delta are used as kernel parameters
- The polynomial kernel of degree d adds a simple non-linear transformation of the data
- The Gaussian RBF kernel is similar to an RBF neural network. The RBF kernel performs well on many types of data and is thought to be a reasonable starting point for many learning tasks
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