Improving the Structuring Capabilities of Statistics-Based Local Learners

摘要

Function approximation, a mainstay of machine learning, is a useful tool in science and engineering. Local learning approches subdivide the learning space into regions to be approximated locally by linear models. An arrangement of regions that conforms to the structure of the target function leads to learning with fewer resources and gives an insight into the function being approximated. This paper introduces a covariance-based update for the size and shape of each local region. An evaluation shows that the method improves the structuring capabilities of state-of-the-art statistics-based local learners.