Approximation Algorithms for Minimizing Empirical Error by Axis-Parallel Hyperplanes

Abstract

Many learning situations involve separation of labeled training instances by hyperplanes. Consistent separation is of theoretical interest, but the real goal is rather to minimize the number of errors using a bounded number of hyperplanes. Exact minimization of empirical error in a high-dimensional grid induced into the feature space by axis-parallel… (More)
DOI: 10.1007/11564096_53

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