Minimizing Statistical Bias with Queries.
David A. Cohn · 1995
I describe a querying criterion that attempts to minimize the error of a learner by minimizing its estimated squared bias. I describe experiments with locally-weighted regression on two simple problems, and observe that this "bias-only" approach outperforms the more common "variance-only" exploration approach, even in the presence of noise. 1 INTRODUCTION In recent years, there has been an explosion of interest in "active" machine learning systems. These are learning systems that make queries, or perform experiments to gather data that are expected to maximize performance. When compared with "passive" learning systems, which accept given, or randomly drawn data, active learners have demonstrated significant decreases in the amount of data required to achieve equivalent performance. In industrial applications, where each experiment may take days to perform and cost thousands of dollars, a method for optimally selecting these points would offer enormous savings in time and money. An act...