Kernel methods from "summary" of Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David
Kernel methods are an advanced set of algorithms used to tackle complex machine learning tasks. These algorithms are capable of finding hidden patterns in data and improve the accuracy and performance of the results. They are extremely powerful tools for data analysis.- Kernel methods are a powerful set of algorithms that can be used for supervised and unsupervised machine learning tasks such as classification, regression, and clustering.
- Kernels rely on the inner product of data points in a so-called feature space, thereby creating non-linear relationships between objects.
- Thanks to kernel methods, computers can now learn complex relations from data without explicit programming instructions.
- Through this process, the machine is able to accurately classify data points based only on their expected relationship with other values and not just individual attributes.
