LaTeX-examples/documents/papers/write-math-paper/ch2-general-system-design.tex

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\section{General System Design}
The following steps are used for symbol classification:\nobreak
\begin{enumerate}
    \item \textbf{Preprocessing}: Recorded data is never perfect. Devices have
          errors and people make mistakes while using the devices. To tackle
          these problems there are preprocessing algorithms to clean the data.
          The preprocessing algorithms can also remove unnecessary variations
          of the data that do not help in the classification process, but hide
          what is important. Having slightly different sizes of the same symbol
          is an example of such a variation. Four preprocessing algorithms that
          clean or normalize recordings are explained in
          \cref{sec:preprocessing}.
    \item \textbf{Data multiplication}: Learning systems need lots of data
          to learn internal parameters. If there is not enough data available,
          domain knowledge can be considered to create new artificial data from
          the original data. In the domain of on-line handwriting recognition,
          data can be multiplied by adding rotated variants.
    \item \textbf{Feature extraction}: A feature is high-level information
          derived from the raw data after preprocessing. Some systems like
          Detexify take the result of the preprocessing step, but many compute
          new features. Those features can be designed by a human engineer or
          learned. Non-raw data features have the advantage that less
          training data is needed since the developer uses knowledge about
          handwriting to compute highly discriminative features. Various
          features are explained in \cref{sec:features}.
\end{enumerate}

After these steps, it is a classification task for which the classifier has to
learn internal parameters before it can classify new recordings.We classified
recordings by computing constant-sized feature vectors and using
\glspl{MLP}. There are many ways to adjust \glspl{MLP} (number of neurons and
layers, activation functions) and their training (learning rate, momentum,
error function). Some of them are described in~\cref{sec:mlp-training} and the
evaluation results are presented in \cref{ch:Optimization-of-System-Design}.