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4 changes: 2 additions & 2 deletions book/thy.tex
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Expand Up @@ -161,7 +161,7 @@ \section{Probably Approximately Correct Learning}
at most $5\%$ error. If this situtation is guaranteed to happen, then
this hypothetical learning algorithm is a PAC learning algorithm. It
satisfies ``probably'' because it only failed in one out of ten cases,
and it's ``approximate'' because it achieved low, but non-zero, error
and its ``approximate'' because it achieved low, but non-zero, error
on the remainder of the cases.

This leads to the formal definition of an $(\ep,\de)$ PAC-learning
Expand Down Expand Up @@ -510,7 +510,7 @@ \section{Complexity of Infinite Hypothesis Spaces}
dimension is the \emph{maximum} number of points for which you can
always find such a classifier.

\thinkaboutit{What is that labeling? What is it's name?}
\thinkaboutit{What is that labeling? What is its name?}

You can think of VC dimension as a game between you and an adversary.
To play this game, \emph{you} choose $K$ unlabeled points however you
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