Incremental learning involves continuous improvement as new data arrives while one-shot or batch learning distinguishes a training phase from the application phase. Supervised learning occurs when the training input has been explicitly labeled with the classes to be learned.
Most learning methods aim to demonstrate generalization whereby the system develops efficient and effective representations that encompass large chunks of closely related data.
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