Learning Qualitative Models from Physiological Signals
David Tak-Wai Hau · 1994
. Physiological models represent a useful form of knowledge, but are both difficult and time consuming to construct by hand. Further, most physiological systems are incompletely understood. This article addresses these two issues with a system that learns qualitative models from physiological signals. The qualitative representation of models allows incomplete knowledge to be encapsulated, and is based on Kuipers' approach used in his QSIM algorithm. The learning algorithm allows automatic generation of such models, and is based on Coiera's GENMODEL algorithm. We first show that QSIM models are efficiently PAC learnable from positive examples only, and that GENMODEL is an algorithm for efficiently constructing a QSIM model consistent with a given set of examples, if one exists. We then describe the learning system in detail, including the front-end processing and segmenting stages that transform a signal into a set of qualitative states, and GENMODEL that uses these qualitative states a...