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For the supervised learning problem, error correcting memorization learning was proposed in order to suppress noise in teacher signals. In this paper, generalization capability of the learning method is discussed. Generalization capability is evaluated based on the projection learning criterion. We give a necessary and sufficient condition for error correcting memorization learning to provide the...
A neural network model that can learn higher order correlations within the input data without suffering from the combinatorial explosion problem is introduced. The number of parameters scales as M ̃×N, where M ̃ is the number such that no higher order network with less than M ̃ higher order terms can implement the same input data set and N is the dimensionality of the input vectors. In order to...
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