Update report_thesis/src/sections/problem_definition.tex

report_thesis/src/sections/problem_definition.tex

@@ -70,5 +70,4 @@ \subsubsection{Data Availability}
 Due to the high cost of data collection, datasets are often small. This limits the number of samples available for evaluation, affecting the generalizability and robustness of the models\cite{p9_paper}.
 
 \subsection{Problem Formulation}
-The objective of this research is to develop a computational model, denoted as $\mathcal{F}: \mathbb{R}^N \rightarrow \mathbb{R}^{n_o}$, to predict major oxide concentrations in geological samples from processed \gls{libs} spectral data.
-This task is complicated by the high dimensionality of the data, multicollinearity among spectral features, matrix effects that alter emission intensities, and the limited availability of data.
+The objective of this research is to develop a computational model, denoted as $\mathcal{F}: \mathbb{R}^N \rightarrow \mathbb{R}^{n_o}$, to predict major oxide concentrations in geological samples from processed \gls{libs} spectral data, that maintains accuracy and exhibits robustness against the challenges posed by the high dimensionality of the data, multicollinearity among spectral features, matrix effects, and the limited availability of data.