In machine learning, a kernel is a method used to transform data into the required form of processing data. It is a set of mathematical functions used in Support Vector Machine (SVM) that provides the window to manipulate the data. Kernels are used to apply linear classifiers to non-linear problems by mapping non-linear data onto a higher-dimensional space without having to visit or understand that higher-dimensional region.
The kernel trick is a method of using a linear classifier to solve a non-linear problem. It entails transforming linearly inseparable data to linearly separable ones. The kernel function is what is applied on each data instance to map the original non-linear observations into a higher-dimensional space in which they become separable.
Kernel functions have been introduced for sequence data, graphs, text, images, as well as vectors. Algorithms capable of operating with kernels include the kernel perceptron, support-vector machines (SVM), Gaussian processes, principal components analysis (PCA), canonical correlation analysis, ridge regression, spectral clustering, linear adaptive filters, and many others.
In summary, a kernel is a method used to transform data into a required form of processing data, and it is used to apply linear classifiers to non-linear problems by mapping non-linear data onto a higher-dimensional space without having to visit or understand that higher-dimensional region.
