MLP ITERATIVE CONSTRUCTION ALGORITHM

This paper presents a novel multi-layer perceptron neural network arch itecture selection and weight training algorithm for classification pr oblems. The MLP iterative construction algorithm (MICA) autonomously c onstructs an MLP neural network as it trains. Experimental results sho w the algorithm achieves 100% accuracy on the training data, the same or better generalization accuracies as Backprop on the test data, whil e using less FLOPS. Moreover, relaxation of the hidden layer nodes imp roves test set recognition accuracies to be greater than that of Backp rop. Furthermore, seeding the Backprop algorithm with the hidden layer weights from MICA is demonstrated. The MICA seeding improves the effe ctiveness of Backprop and enables Backprop to solve a new class of pro blems, i.e., problems with areas of low mean-squared error.