Definition: PseudoJervine is a term used in the context of machine learning and artificial intelligence to refer to a type of model that has been trained on limited data, but is still able to perform well on new, unseen data. This type of model is known as an overfitting model because it does not generalize well to new, unseen data. PseudoJervine models can be used for tasks such as image classification, natural language processing, and speech recognition, where the goal is to
what should be said.