Efficient active learning with generalized linear models
Jeremy Lewi, Robert J. Butera, Liam Paninski · 2007
Active learning can significantly reduce the amount of training data required to fit parametric statistical models for supervised learning tasks. Here we present an efficient algorithm for choosing the optimal (most informative) query when the output labels are related to the inputs by a generalized linear model (GLM). The algorithm is based on a Laplace approximation of the posterior distribution of the GLM’s parameters. The algorithm requires only low-rank matrix manipulations and a single two-dimensional search to choose the optimal query and has complexity O(n 2) (with n the dimension of the