A non-linear learning & classification algorithm that achieves full training accuracy with stellar classification accuracy
This work presents a new algorithm for classification tasks, but it appears incremental as it builds on existing methods and focuses on specific datasets without broad application.
The authors tackled the problem of learning and classification by proposing the Reverse Ripple Effect (R.R.E) algorithm, a non-linear and non-iterative method that achieves 100% training accuracy and is evaluated against other algorithms like Perceptron, SVM, and Neural Networks on datasets including the XOR problem.
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.