What is kernel? How kernel can be used with SVM to classify non-linearly separable data?
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What is a kernel?

The kernel is the essential foundation of a computer's operating system (OS). It is the core that provides basic services for all other parts of the OS.

It is the main layer between the OS and underlying computer hardware, and it helps with tasks such as process and memory management, file systems, device control, and networking.


In machine learning, support vector machines (SVMS, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

It is a programming computer to optimize a performance criterion using example data or past experience.

Supervised learning problems are defined as an input X, an output Y, and the task is to learn to map from the input to the output,

$$ y=g(x \mid \varnothing) $$

Non-linear classification:

Data sets that are linearly separable can be dealt with very conveniently.

But what do we do if the data set just doesn't allow classification by linear classifier?

For e.g. The image below describes how convenient it is to separate linear data in comparison to nonlinear data.

Nonlinear classification is done where classes are not linearly separable by a boundary. Linear vs. nonlinear problems

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