%!TEX root = thesis.tex %Term definitions \newacronym{ANN}{ANN}{artificial neural network} \newacronym{CSR}{CSR}{cursive script recognition} \newacronym{DTW}{DTW}{dynamic time warping} \newacronym{GTW}{GTW}{greedy time warping} \newacronym{HMM}{HMM}{hidden Markov model} \newacronym{HWR}{HWR}{handwriting recognition} \newacronym{HWRT}{HWRT}{handwriting recognition toolkit} \newacronym{MLP}{MLP}{multilayer perceptron} \newacronym{MSE}{MSE}{mean squared error} \newacronym{OOV}{OOV}{out of vocabulary} \newacronym{TDNN}{TDNN}{time delay neural network} \newacronym{PCA}{PCA}{principal component analysis} \newacronym{LDA}{LDA}{linear discriminant analysis} \newacronym{CROHME}{CROHME}{Competition on Recognition of Online Handwritten Mathematical Expressions} \newacronym{GMM}{GMM}{Gaussian mixture model} \newacronym{SVM}{SVM}{support vector machine} \newacronym{PyPI}{PyPI}{Python Package Index} \newacronym{CFM}{CFM}{classification figure of merit} \newacronym{CE}{CE}{cross entropy} \newacronym{GPU}{GPU}{graphics processing unit} \newacronym{CUDA}{CUDA}{Compute Unified Device Architecture} \newacronym{SLP}{SLP}{supervised layer-wise pretraining} % Term definitions \newglossaryentry{Detexify}{name={Detexify}, description={A system used for on-line handwritten symbol recognition which is described in \cite{Kirsch}}} \newglossaryentry{epoch}{name={epoch}, description={During iterative training of a neural network, an \textit{epoch} is a single pass through the entire training set, followed by testing of the verification set.\cite{Concise12}}} \newglossaryentry{hypothesis}{ name={hypothesis}, description={The recognition results which a classifier returns is called a hypothesis. In other words, it is the \enquote{guess} of a classifier}, plural=hypotheses } \newglossaryentry{reference}{ name={reference}, description={Labeled data is used to evaluate classifiers. Those labels are called references}, } \newglossaryentry{YAML}{name={YAML}, description={YAML is a human-readable data format that can be used for configuration files}} \newglossaryentry{MER}{name={MER}, description={An error measure which combines symbols to equivalence classes. It was introduced on \cpageref{merged-error-introduction}}} \newglossaryentry{JSON}{name={JSON}, description={JSON, short for JavaScript Object Notation, is a language-independent data format that can be used to transmit data between a server and a client in web applications}} \newglossaryentry{hyperparamter}{name={hyperparamter}, description={A \textit{hyperparamter} is a parameter of a neural net, that cannot be learned, but has to be chosen}, symbol={\ensuremath{\theta}}} \newglossaryentry{learning rate}{name={learning rate}, description={A factor $0 \leq \eta \in \mdr$ that affects how fast new weights are learned. $\eta=0$ means that no new data is learned}, symbol={\ensuremath{\eta}}} % Andrew Ng: \alpha \newglossaryentry{learning rate decay}{name={learning rate decay}, description={The learning rate decay $0 < \alpha \leq 1$ is used to adjust the learning rate. After each epoch the learning rate $\eta$ is updated to $\eta \gets \eta \times \alpha$}, symbol={\ensuremath{\eta}}} \newglossaryentry{preactivation}{name={preactivation}, description={The preactivation of a neuron is the weighted sum of its input, before the activation function is applied}} \newglossaryentry{stroke}{name={stroke}, description={The path the pen took from the point where the pen was put down to the point where the pen was lifted first}} \newglossaryentry{line}{name={line}, description={Geometric object that is infinitely long and defined by two points.}} \newglossaryentry{line segment}{name={line segment}, description={Geometric object that has finite length and defined by two points.}} \newglossaryentry{symbol}{name={symbol}, description={An atomic semantic entity. A more detailed description can be found in \cref{sec:what-is-a-symbol}}} \newglossaryentry{weight}{name={weight}, description={A \textit{weight} is a parameter of a neural net, that can be learned}, symbol={\ensuremath{\weight}}} \newglossaryentry{control point}{name={control point}, description={A \textit{control point} is a point recorded by the input device.}}