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fixes in a few chapters

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This commit is contained in:
Jan Grewe
2018-11-02 11:05:02 +01:00
parent 5cf23aba85
commit 79f282f7b3
4 changed files with 8 additions and 10 deletions
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regression/lecture/regression.tex
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@@ -86,7 +86,7 @@ large deviations.
$f_{cost}(\{(x_i, y_i)\}|\{y^{est}_i\})$ is a so called
\enterm{objective function} or \enterm{cost function}. We aim to adapt
the model parameters to minimize the error (mean square error) and
thus the \emph{objective function}. In Chapter~\ref{maximumlikelihood}
thus the \emph{objective function}. In Chapter~\ref{maximumlikelihoodchapter}
we will show that the minimization of the mean square error is
equivalent to maximizing the likelihood that the observations
originate from the model (assuming a normal distribution of the data
@@ -270,7 +270,7 @@ The gradient is given by partial derivatives
(Box~\ref{partialderivativebox}) with respect to the parameters $m$
and $b$ of the linear equation. There is no need to calculate it
analytically but it can be estimated from the partial derivatives
using the difference quotient (Box~\ref{differentialquotient}) for
using the difference quotient (Box~\ref{differentialquotientbox}) for
small steps $\Delta m$ und $\Delta b$. For example the partial
derivative with respect to $m$: