Abstract

The performance of Back Propagation methods strongly depend on the following two choices: (1) use of off-line or on-line algorithm; (2) level of redundancy of the training set of data. Past investigations studied respectively off-line algorithms with a low degree of redundancy and on- line algorithms with a high degree of redundancy. In this paper we complete the framework considering on-line algorithms with a low level of information and off-line algorithms using training sets with 'redundancy of target data'.

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