Designing neural network architectures for pattern recognition

An appropriately designed architecture of a neural network is essential to many realistic pattern-recognition tasks. A choice of just the right number of neurons, and their interconnections, can cut learning cos ts by orders of magnitude, and still warrant high classification accuracy. Surprisingly, textbooks often neglect this issue. A specialist seeking systematic inform ation will soon realize that relevant material is scattered over diverse so urces, each with a different perspective, terminology and goals. This brief survey attempts to rectify the situation by explaining the involved aspect s, and by describing some of the fundamental techniques.