Classification for Dynamical Systems: Model-based Approach and Support Vector Machines
arXiv:1803.105521 citationsh-index: 42
Originality Synthesis-oriented
AI Analysis
For researchers in control and machine learning, this provides a comparative analysis of two classification paradigms for dynamical systems.
The paper compares model-based and SVM-based approaches for classifying trajectories from dynamical systems, revealing connections and trade-offs between the two methods.
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.