@article {IOPORT.00759415,
    author       = {Anthony, Martin and Holden, Sean B.},
    title        = {Quantifying generalization in linearly weighted neural networks.},
    year         = {1994},
    journal      = {Complex Systems},
    volume       = {8},
    number       = {2},
    issn         = {0891-2513},
    pages        = {91-114},
    publisher    = {Complex Systems Publications, Inc., Champaign, IL},
    abstract     = {Summary: The Vapnik-Chervonenkis dimension has proven to be of great use in the theoretical study of generalization in artificial neural networks. The ``probably approximately correct'' learning framework is described and the importance of the Vapnik-Chervonenkis dimension is illustrated. We then investigate the Vapnik-Chervonenkis dimension of certain types of linearly weighted neural networks. First, we obtain bounds on the Vapnik- Chervonenkis dimensions of radial basis function networks with basis functions of several types. Secondly, we calculate the Vapnik- Chervonenkis dimension of polynomial discriminant functions defined over both real and binary-valued inputs.},
    identifier   = {00759415},
}