From 0e9a9b8bf0104b573d04cca4438b905022a4ea06 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Tue, 30 Oct 2012 16:48:49 +0100
Subject: Cleanup of the main section

---
 problem.tex | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'problem.tex')

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:
-- 
cgit v1.2.3-70-g09d2

