Following the foundational development of the Vapnik-Chervonenkis (VC) dimension, researchers introduced numerous alternative complexity measures to quantify the flexibility of model classes in statistical learning theory. Like the VC dimension, each of these advanced measures facilitates analogous mathematical guarantees for the generalization gap. However, despite their widespread utility in traditional statistical theory, these alternative function complexity metrics, when straightforwardly applied, similarly fail to explain the phenomenon of why highly parameterized deep neural networks generalize effectively in practice.

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The Vapnik-Chervonenkis (VC) dimension is a metric that quantifies the complexity or flexibility of a model class. It is defined as the maximum number of data points for which the model class can perfectly classify any arbitrary binary labeling; that is, for any possible assignment of labels to these points, there exists some classifier $$f$$ in the class that correctly matches that labeling.

Vapnik-Chervonenkis (VC) Dimension

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For linear models operating on $$d$$-dimensional inputs, the Vapnik-Chervonenkis (VC) dimension is exactly $$d+1$$. For instance, a linear boundary can easily shatter, or assign any possible binary labeling to, three non-collinear points in two dimensions, but it cannot shatter four points.

Vapnik-Chervonenkis (VC) Dimension of Linear Models

A key result in statistical learning theory bounds the generalization gap between the empirical error $$R_	extrm{emp}$$ and the true error $$R$$ as a function of the Vapnik-Chervonenkis (VC) dimension and the dataset size $$n$$. With a probability of at least $$1-\delta$$, the generalization gap is strictly less than $$\alpha$$, provided that $$\alpha \geq c \sqrt{(	extrm{VC} - \log \delta)/n}$$, where $$c > 0$$ is a constant depending on the loss scale. While theoretically profound, this bound often provides a pessimistic estimate for complex models.

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