This paper presents new results about the confidence bounds on the generalization performances of perceptrons. It deals with regression problems. It is shown that the probability to get a generalization error greater than the empirical error plus a precision ε, depends on the number of inputs and on the magnitude of the coefficients of the combination. The result presented does not require to bound a priori the magnitude of these coefficients, the size and the number of layers.
CITATION STYLE
Gavin, G. (2001). Generalization performances of perceptrons. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2130, pp. 259–264). Springer Verlag. https://doi.org/10.1007/3-540-44668-0_37
Mendeley helps you to discover research relevant for your work.