During the learning process, it is important to design how the training should be performed. Two basic approaches are batch and incremental learning.
In batch learning, all the records are fed to the network, so it can evaluate the error and then update the weights:
In incremental learning, the update is performed after each record has been sent to the network:
Both approaches work well and have advantages and disadvantages. While batch learning can used for a less frequent, though more directed, weight update, incremental learning ...