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VC dimension
VC維 (Vapnik–Chervonenkis dimension)


在VC理論中,VC維是對一個可學習分類函數空間的能力的衡量。它定義為算法能「打散」的點集的勢的最大值。 直觀地,一個分類模型的能力與其複雜程度相關。例如,考慮一個高次多項式的分類模型:若函數值大於0則分類為正,反之則分類為負。高次多項式能夠「擺動」的範圍很大,所以能夠很好地擬合給定的點集。 维基百科
In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis (VC) dimension is a measure of the capacity of a set of functions that can be learned by a ...


The VC dimension of a classifier is defined by Vapnik and Chervonenkis to be the cardinality (size) of the largest set of points that the classification ...
learnable and derive the right bound? The answer is yes and it is based on theVapnik-. Chervonenkis dimension (VC-dimension, [Vapnik.
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Exercise: Show that the VC dimension of linear threshold functions in n dimensions is n+ 1. 2 Growth Function. Let C be a concept class, we define the growth ...
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作者:SC Tenka2021 — the VC-Dimension? Samuel C. Tenka. Wetzel's Cake Problem. Combinatorists and bakers alike know the sequence. 1, ...
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10-701, Fall 2015. VC Dimension and Model. Complexity. Eric Xing. Lecture 16, November 3, 2015. Reading: Chap. 7 T.M book, and outline material ...
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The VC-dimension of a function class is defined as the maximal number of points that can be shattered by this function class. In other words, it is the largest ...

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