Nonlinear Projection and Overfitting

Kyle and Henning discuss the concept of nonlinear projection in machine learning, drawing parallels to support vector machines and Covers theorem. They also explore the potential advantages and disadvantages of smaller parameter sets in models, highlighting the risk of overfitting in Koopman algorithms.