# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 5 Principal Results
### Findings on Machine Learning
#### 5.1.1 Algorithm Selection and Classification  
The way learning is achieved, i.e., the algorithm used, can be categorized into learning model paradigms. Using the broadest definition, four paradigms, as shown in Table 3, are generally identified: Supervised Learning (SL), Unsupervised Learning (USL), Semi-Supervised Learning (SSL), and Reinforcement Learning [26]. As can be seen from the definitions, this classification of learning models is based on the labeling and rules known from the available information, as well as the way feedback is given or processed. Our findings show that all paradigms are used in the context of HDTs. In some cases, multiple algorithms are used, which do not have to be of the same paradigm. In these cases, using more specific definitions and criteria, looking, for example, at the character of the feedback, or the structure and architecture of the algorithm, other paradigms can be defined, such as multitask learning, evolutionary learning, ensemble learning, instance-based learning, dimensionality reduction algorithms, hybrid learning, neural networks, and deep learning [25; 27; 2]. The classification of ML algorithms can be refined by looking at the objective of the algorithm [25; 26]. These objectives can be divided into three categories: Data, Solution, and Mixed. The two types of problems that belong to the data category focus on improving \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Publications**} \\ \hline Machine learning algorithms & general & [39; 40; 41; 42; 43; 44; 45] \\ \cline{2-3}  & selection & [25; 26; 27; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50] \\ \cline{2-3}  & performance & [8; 29; 51; 52; 53; 54; 55; 56; 57; 58; 59] \\ \cline{2-3}  & both & [28; 60; 61; 62; 63] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Articles found on machine learning. the processing of the data by obtaining information about the structure of the input data or modifying the data. These types include dimensionality reduction and feature learning, as well as association rule learning. The five types that focus on processing the data into new information form the solution category: Classification analysis, which assigns a class label to an input data point based on its features. Regression analysis models the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. Cluster analysis involves exploring and analyzing the structure of a dataset, identifying patterns, and discovering natural groupings of data points. Reinforcement learning uses rewards and punishments to teach an AI agent how to behave in an environment. Anomaly detection involves identifying data points that are unusual or out of the ordinary compared to the rest of the data. The last category mentioned is the mixed category, which contains problems involving artificial neural networks and deep learning, where a combination of the other categories is used. This classification is summarized in Table 4. \begin{table}
\begin{tabular}{c l} \hline \hline
**Objective** & **Type** \\ \hline Data & Dimensionality Reduction and Feature Learning \\ \cline{2-3}  & Association Rule Learning \\ \hline Solution & Classification \\ \cline{2-3}  & Regression \\ \cline{2-3}  & Clustering \\ \cline{2-3}  & Reinforcement Learning \\ \cline{2-3}  & Anomaly Detection \\ \hline Mixed & Deep Learning \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification of ML techniques based on the objective.