MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
Abstract
We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both statistically-significant functions (i.e., patterns) when the...
Paper Details
Title
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
Published Date
Jun 16, 2020
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