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Extensions of a Theory of Networks for Approximation and Learning: Dimensionality Reduction and Clustering
The theory developed in Poggio and Girosi (1989) shows the equivalence between regularization and a class of three-layer networks that we call regularization networks or Hyper Basis Functions. These networks are also ...
Extensions of a Theory of Networks for Approximation and Learning: Outliers and Negative Examples
Learning an input-output mapping from a set of examples can be regarded as synthesizing an approximation of a multi-dimensional function. From this point of view, this form of learning is closely related to regularization ...
Continuous Stochastic Cellular Automata that Have a Stationary Distribution and No Detailed Balance
Marroquin and Ramirez (1990) have recently discovered a class of discrete stochastic cellular automata with Gibbsian invariant measures that have a non-reversible dynamic behavior. Practical applications include more ...