  We analyze a new approach to Machine Learning coming from a modification of
classical regularization networks by casting the process in the time dimension,
leading to a sort of collapse of dimensionality in the problem of learning the
model parameters. This approach allows the definition of a online learning
algorithm that progressively accumulates the knowledge provided in the input
trajectory. The regularization principle leads to a solution based on a
dynamical system that is paired with a procedure to develop a graph structure
that stores the input regularities acquired from the temporal evolution. We
report an extensive experimental exploration on the behavior of the parameter
of the proposed model and an evaluation on artificial dataset.
