This work presents a constructive method to train the multilayer perceptron layer after layer successively and to accomplish the kernel used in the support vector machine. Data in different classes will be trained to map to distant points in each layer. This will ease the mapping of the next layer. A perfect mapping kernel can be accomplished successively. Those distant mapped points can be dis...

This paper presents a class of neural networks suitable for the application of decoding error-correcting codes.The neural model is basically a perceptron with a high-order polynomial as its discriminant function. A single layer of high-order perceptrons is shown to be able to decode a binary linear block code with at most 2 weights in each perceptron, where m is the parity length. For some subc...

optical coherence tomography (oct) uses the spatial and temporal coherence properties of optical waves backscattered from a tissue sample to form an image. an inherent characteristic of coherent imaging is the presence of speckle noise. in this study we use a new ensemble framework which is a combination of several multi-layer perceptron (mlp) neural networks to denoise oct images. the noise is...