The process whereby a neural network learns to associate an input pattern with an output pattern, by comparing actual responses against correct ones, is called:

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The process of associating input and output patterns in a neural network is known as training. Backward propagation is a common algorithm used in neural network training where errors in the output are propagated backwards through the network to adjust the weights of the connections between neurons. This helps the network to learn and improve its accuracy over time. Forward propagation is the process of inputting data into the neural network and obtaining an output, while retroactive and proactive interference refer to specific types of memory interference in cognitive psychology. Retrospective learning is not a well-known term in the field of neural networks.

The Process Whereby a Neural Network Learns to Associate an Input