The Cascade-Correlation learning architecture

作者：Scott E. Fahlman, Christian J. Lebiere · 年份：2018 · DOI：10.1184/r1/6610403 · 被引用次数：2394 · 研究领域：Neural Networks and Applications、Face and Expression Recognition、Image Retrieval and Classification Techniques

Abstract: "Cascade-Correlation is a new architecture and supervised learning algorithm for artificial neural networks. Instead of just adjusting the weights in a network of fixed topology, Cascade-Correlation begins with a minimal network, then automatically trains and adds new hidden units one by one, creating a multi-layer structure. Once a new hidden unit has been added to the network, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the network, available for producing outputs or for creating other, more complex feature detectors. The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network."