Multiclass pattern classification using neural networks

Multiclass neural learning involves finding appropriate neural network architecture, encoding schemes, learning algorithms, etc. We discuss major approaches used in neural networks for classifying multiple classes. The discussion is focused on these architectures using either a system of multiple neural networks or a single neural network. We discuss various learning algorithms, one-again-all, one-against-one, and p-against-q. We also discuss training procedures associated with each approach, implementation and time complexity. These methods are evaluated through their performances on the NlST handwritten digit database.