n particle physics, supervised learning encompasses techniques such as boosted decision trees and deep neural nets. These techniques learn non-linear models that are accurate yet expensive, hard to visualize and hard to interpret. An alternative approach is to transform data into a higher-dimensional space and learn a linear model in that space.
Although not commonly used in practice, this approach can offer a number of advantages such as simplified parameter tuning, quick training on large datasets and ease of interpretation due to the linear nature of constructed models. In this talk, a linear technique in this class is presented in the context of binary classification.