Convergence
In machine learning, a model has converged when its weights have settled and further training produces no improvement.
In machine learning, a model has converged when its weights have settled and further training produces no improvement. The Perceptron Convergence Theorem guarantees convergence for linearly separable data. For non-separable data, the Perceptron never converges — it keeps oscillating.