Learning algorithms in neural networks: recent results

摘要: We review two new algorithms for learning in neural networks of Boolean units. The first applies to the problem associative memory: Hopfield model or perception. algorithm optimizes stability learned patterns, which enlarges size basins attraction. second builds a multilayer feedforward network: it allows one learn an arbitrary mapping input → output. convergence growth process is guaranteed. generalization properties look very promising.