Which option refers to the method that continues to train until convergence or the maximum iteration count?

The method that continues to train until convergence or reaches the maximum iteration count is commonly referred to as "Nonstop training." This approach is designed to keep the training process active until the model achieves an optimal state, which is defined as either reaching a convergence threshold or hitting a predefined limit on the number of iterations.

In contrast to other training methods, "Nonstop training" allows flexibility to improve model performance by considering both the accuracy of predictions and the limits set by the user regarding training duration. This can lead to a more refined model that better generalizes to new data.

The other options do not capture this continuous nature of training effectively; for instance, "Stopped training" implies that the training has already ceased, while "Fixed iteration training" suggests that training is limited to a predetermined number of iterations without considering convergence. "Adaptive training" might indicate a method that adjusts based on performance but does not necessarily imply it continues until the optimal state is achieved.