# Classification for Dynamical Systems: Model-based Approach and Support Vector Machines

## TL;DR

This paper compares model-based and data-driven support vector machine approaches for classifying trajectories of dynamical systems, highlighting their connections and relative advantages.

## Contribution

It provides a comparative analysis of control engineering and machine learning methods for dynamical system classification, revealing their links and benefits.

## Key findings

- Connections between model-based and SVM approaches identified
- Analysis of relative merits of both methods
- Insights into combining control and machine learning techniques

## Abstract

We consider the problem of classifying trajectories generated by dynamical systems. We investigate a model-based approach, the common approach in control engineering, and a data-driven approach based on Support Vector Machines, a popular method in the area of machine learning. The analysis points out connections between the two approaches and their relative merits.