Hierarchical Autoassociative Polynimial Network (HAP Net) for pattern recognition

Extensive research and evaluations have been conducted on neural networks to improve classification accuracy and training time. Many classical architectures of neural networks have been modified in several different ways for advancement in design. We propose a new architecture, the hierarchical autoassociative polynomial neural network (HAP Net), which is a formulation of different neural network concepts. HAP Net is a combination of polynomial networks, which provides the network with nonlinear weighting, deep belief networks, which obtains higher level abstraction of the incoming data, and convolutional neural networks, which localizes regions of neurons. By incorporating all of these concepts together along with a derivation of a standard backpropagation algorithm, we produce a strong neural network that has the strengths of each concept. Evaluations have been conducted on the MNIST Database, which is a well-known character database tested by many state of the art classification algorithms, and have found the HAP Net to have one of the lowest test error rates among many leading algorithms.