The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis

A distinction between two forms of task knowledge transfer, representational and functional, is reviewed followed by a discussion of MTL, a modiied version of the multiple task learning (MTL) neural network method of functional transfer. The MTL method employs a separate learning rate, k , for each task output node k. k varies as a function of a measure of relatedness, R k , between the kth task and the primary task of interest. An MTL network is applied to a diagnostic domain of four levels of coronary artery disease. Results of experiments demonstrate the ability of MTL to develop a predictive model for one level of disease which has superior diagnostic ability over models produced by either single task learning or standard multiple task learning.