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Discrete Mathematics & Theoretical Computer Science,Vol 10, No 1 (2008)

机译:离散数学与理论计算机科学,第10卷,第1期(2008年)

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For any class of binary functions on [n]={1, …, n} a classical result by Sauer states a sufficient condition for its VC-dimension to be at least d: its cardinality should be at least O(nd-1). A necessary condition is that its cardinality be at least 2d (which is O(1) with respect to n). How does the size of a `typical' class of VC-dimension d compare to these two extreme thresholds ? To answer this, we consider classes generated randomly by two methods, repeated biased coin flips on the n-dimensional hypercube or uniform sampling over the space of all possible classes of cardinality k on [n]. As it turns out, the typical behavior of such classes is much more similar to the necessary condition; the cardinality k need only be larger than a threshold of 2d for its VC-dimension to be at least d with high probability. If its expected size is greater than a threshold of O(&log;n) (which is still significantly smaller than the sufficient size of O(nd-1)) then it shatters every set of size d with high probability. The behavior in the neighborhood of these thresholds is described by the asymptotic probability distribution of the VC-dimension and of the largest d such that all sets of size d are shattered.
机译:对于[n] = {1,…,n}上的任何二元函数类,Sauer的经典结果都说明其VC维数至少为d的充分条件:其基数应至少为O(nd-1) 。必要条件是其基数至少为2d(相对于n为O(1))。 VC维d的“典型”类别的大小与这两个极限阈值相比如何?为了回答这个问题,我们考虑通过两种方法随机生成的类,即在n维超立方体上重复偏置硬币翻转或在[n]上所有可能的基数k的空间上进行均匀采样。事实证明,此类的典型行为与必要条件更为相似。基数k仅需要大于2d的阈值,其VC维度才能以高概率至少为d。如果其预期大小大于O(logn)的阈值(仍远小于O(nd-1)的足够大小),则它很有可能破坏每一个大小d的集合。这些阈值附近的行为通过VC维和最大d的渐近概率分布来描述,从而使所有大小为d的集合都被粉碎。

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