# PAC: Probably Approximately Correct

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Category: { Machine Learning::Theories }
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Summary:
Pages: 5

# SRM: Structural Risk Minimization

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Category: { Machine Learning::Theories }
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Pages: 5

# The Learning Problem

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Category: { Machine Learning::Theories }
Summary: The learning problem posed by Vapnik:1 Given a sample: $\{z_i\}$ in the probability space $Z$; Assuming a probability measure on the probability space $Z$; Assuming a set of functions $Q(z, \alpha)$ (e.g. loss functions), where $\alpha$ is a set of parameters; A risk functional to be minimized by tunning “the handles” $\alpha$, $R(\alpha)$. The risk functional is $$R(\alpha) = \int Q(z, \alpha) \,\mathrm d F(z).$$ A learning problem is the minimization of this risk.
Pages: 5

# Cross Validation

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Category: { Machine Learning::Theories }
Summary: Cross validation is a method to estimate the risk The learning problem posed by Vapnik:1 Given a sample: $\{z_i\}$ in the probability space $Z$; Assuming a probability measure on the probability space $Z$; Assuming a set of functions $Q(z, \alpha)$ (e.g. loss functions), where $\alpha$ is a set of parameters; A risk functional to be minimized by tunning “the handles” $\alpha$, $R(\alpha)$. The risk functional is  R(\alpha) = \int Q(z, \alpha) \,\mathrm d F(z).
Pages: 5