Kernel-Based Learning

Learn about kernal-based functions to transform the input data for Support Vector Machine (SVM).

SVM is a kernel-based algorithm. A kernel is a function that transforms the input data to a high-dimensional space where the problem is solved. Kernel functions can be linear or nonlinear.

Oracle Machine Learning for SQL supports linear and Gaussian (nonlinear) kernels.

In Oracle Machine Learning for SQL, the linear kernel function reduces to a linear equation on the original attributes in the training data. A linear kernel works well when there are many attributes in the training data.

The Gaussian kernel transforms each case in the training data to a point in an n-dimensional space, where n is the number of cases. The algorithm attempts to separate the points into subsets with homogeneous target values. The Gaussian kernel uses nonlinear separators, but within the kernel space it constructs a linear equation.

Note:

Active Learning is not relevant in Oracle Database 12c Release 2 and later. A setting similar to Active Learning is ODMS_SAMPLING.