Neutral nets for computing
Richard P. Lippmann · 2003
There has been a resurgence of interest in neutral net models composed of many simple interconnected processing elements operating in parallel. The computational power of different neutral net models and the effectiveness of simple error correction training procedures have been demonstrated. Three important feed-forward models are described. Single- and multi-layer perceptrons which can be used for pattern classification are described, as well as Kohonen's feature map algorithm which can be used for clustering or as a vector quantizer. A major emphasis is placed on relating these models to existing classification and clustering algorithms.>