  A fast Non-linear and non-iterative learning and classification algorithm is
synthesized and validated. This algorithm named the "Reverse Ripple
Effect(R.R.E)", achieves 100% learning accuracy but is computationally
expensive upon classification. The R.R.E is a (deterministic) algorithm that
super imposes Gaussian weighted functions on training points. In this work, the
R.R.E algorithm is compared against known learning and classification
techniques/algorithms such as: the Perceptron Criterion algorithm, Linear
Support Vector machines, the Linear Fisher Discriminant and a simple Neural
Network. The classification accuracy of the R.R.E algorithm is evaluated using
simulations conducted in MATLAB. The R.R.E algorithm's behaviour is analyzed
under linearly and non-linearly separable data sets. For the comparison with
the Neural Network, the classical XOR problem is considered.
