Concept Learning with Geometric Hypotheses

Report ID: TR-563-95

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Author: Gunopulos, Dimitrios / Dobkin, David P.

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Date: 1995-12-00

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Pages: 8

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Download Formats: |Postscript|

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Abstract:

We present a general approach to solving the minimizing disagreement problem for geometric hypotheses with finite VC-dimension. These results also imply efficient agnostic-PAC learning of these hypotheses classes. In particular we give an O(n^{min{alpha + ½ , 2k-1}} log n) algorithm that solves the m.d.p. for two-dimensional convex k-gon hypotheses (where alpha is the VC dimension of the implied set system, k is constant), and an O(n^3k-1log n) algorithm for convex k-hedra hypotheses in three dimensions. We extend these results to handle unions of k-gons and give an approach to approximation algorithms.