**5. Learning and generalization**

Learning and generalization are two specifics properties that characterize any neural network. Unlike traditional methods that build programs to solve a problem, neural network operates mainly on a learning basis. We do not program a neural network, but we learn it. This is why the learning phase is among the most important properties of neural network.

The learning phase consists to estimate the parameter of the network in such a way that it can best fulfill the task assigned to it. This phase cannot be effective only after having accumulated a set of inputs/outputs. When creating a neural network, the inputs and outputs are fixed relative to the application to be accomplished, it is the network weights that are modified and adjusted during the learning phase. The weight adjustment cannot be done in a random way but according to a "learning algorithm." The generalization phase, known also the test phase, is one of the characteristics that determines the neural network performance. It consists to treat the output network with respect to the nonlearned inputs. The network generalization capacity degrades in the case of under/on learning.
