Windowed active sampling for reliable neural learning

The composition of the example set has a major impact on the quality of neural learning. The popular approach is focused on extensive preprocessing to bridge the representation gap between process measurement and neural presentation. In contrast, windowed active sampling attempts to solve these problems in an on–line interaction between problem selection and learning. This paper provides an unified view on the conflicts that may pop–up within a neural network in the presence of ill–ordered data. It is marked that such conflicts become noticeable from the operational learning characteristics. An adaptive operational strategy is proposed that closes the representation gap and its working is illustrated in the diagnosis of power generators.

• Type: Journal
• Year: 1998
• Where: JSA
• Authors: Emilia I. Barakova, Lambert Spaanenburg
• Keywords: Active Sampling Attempts, JSA 1998, Neural, Representation Gap