A new F-score gradient-based training rule for the linear model

摘要

Delta rule is a standard, well-established approach to train perceptron recognition model. However, mean squared error, on which it is based, is not suitable estimate for some problems, like information retrieval or automatic data annotation. F-score, a combination of precision and recall, is one of the major quality measures and can be used as an alternative. In this paper we present perceptron training model based on f-score. An approximate of f-score is proposed, based on components which are both continuous and differentiable. It allows to formulate a gradient-descent training routine, conceptually similar to the standard delta rule.