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diff --git a/problem.tex b/problem.tex index db7108b..f5917f2 100644 --- a/problem.tex +++ b/problem.tex @@ -46,7 +46,7 @@ problem: This optimization, commonly known as \emph{ridge regression}, reduces to a least squares fit for $\mu=\infty$. For finite $\mu$, ridge regression acts as a sort of ``Occam's razor'', favoring a \emph{parsimonious} model for $\beta$: among two vectors with the same square error, the one with the smallest norm is preferred. This is consistent with the Gaussian prior on $\beta$, which implies that vectors with small norms are more likely. %In practice, ridge regression is known to give better prediction results over new data than model parameters computed through a least squares fit. -\subsection{A Budgeted Auction} +\subsection{A Budgeted Auction}\label{sec:auction} TODO Explain the optimization problem, why it has to be formulated as an auction problem. Explain the goals: |