[KB-309] intro updated #217
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Pattrigue
reviewed
Jun 12, 2024
Pattrigue
reviewed
Jun 12, 2024
Pattrigue
reviewed
Jun 12, 2024
Pattrigue
reviewed
Jun 12, 2024
Pattrigue
reviewed
Jun 12, 2024
Pattrigue
reviewed
Jun 12, 2024
Co-authored-by: Pattrigue <[email protected]>
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Co-authored-by: Pattrigue <[email protected]>
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Deployment failed with the following error: |
Co-authored-by: Pattrigue <[email protected]>
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Deployment failed with the following error: |
Co-authored-by: Pattrigue <[email protected]>
Co-authored-by: Pattrigue <[email protected]>
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Deployment failed with the following error: |
Co-authored-by: Pattrigue <[email protected]>
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Deployment failed with the following error: |
Pattrigue
reviewed
Jun 12, 2024
Co-authored-by: Pattrigue <[email protected]>
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Deployment failed with the following error: |
Pattrigue
approved these changes
Jun 12, 2024
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Built PDF for this PR: Download PDF Commit: |
Pattrigue
reviewed
Jun 12, 2024
Co-authored-by: Pattrigue <[email protected]>
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Built PDF for this PR: Download PDF Commit: |
chhoumann
reviewed
Jun 12, 2024
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I'm not sure I think it's quite there. What about this?
In the following sections, we provide a comprehensive exploration of our research.
Section~\ref{sec:related-work} reviews the existing literature on \gls{libs} data analysis and machine learning models, highlighting previous approaches and their limitations.
Section~\ref{sec:problem_definition} formally defines the problem we address, focusing on the challenges of high dimensionality, multicollinearity, and matrix effects in \gls{libs} data.
Section~\ref{sec:background} offers background information on the data, as well as the preprocessing techniques and machine learning models that were used.
In Section~\ref{sec:baseline_replica}, we describe the baseline model used for Martian geological sample analysis, our efforts to replicate it, and the modifications made to improve its performance.
It is used as a baseline to evaluate our proposed stacking ensembles against.
Section~\ref{sec:proposed_approach} presents our proposed approach for optimizing pipeline configurations, detailing the selection of models and preprocessing techniques, our approach to data partitioning, validation and testing procedures, and the implementation of the hyperparameter optimization framework.
Section~\ref{sec:methodology} presents the design and results of our experiments, as well as the analysis of the results.
Our experiments include initial model selection, hyperparameter optimization, and the final evaluation of our proposed stacking ensemble.
Finally, Section~\ref{sec:conclusion} summarizes our key findings and contributions, while Section~\ref{sec:future_work} discusses potential future research directions and improvements.| The remainder of this paper is organized as follows: | ||
| Section~\ref{sec:background} provides background on the onoging Mars exploration missions, the \gls{libs} technique, and the baseline \gls{moc} model. | ||
| In Section~\ref{sec:related-work}, we review the related work on quantitative analysis in the field of \gls{libs} data analysis. |
| Section~\ref{sec:results} presents the results of our experiments. | ||
| Section~\ref{sec:future_work} discusses potential future work. | ||
| Finally, Section~\ref{sec:conclusion} concludes the paper by summarizing our findings and discussing the implications of our work. | ||
| Section~\ref{sec:background} provides an overview of the data used in this work, the preprocessing techniques, and the machine learning models and techniques employed in our proposed pipeline. |
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Not just in the proposed pipeline. We aren't just proposing a pipeline.
| Section~\ref{sec:future_work} discusses potential future work. | ||
| Finally, Section~\ref{sec:conclusion} concludes the paper by summarizing our findings and discussing the implications of our work. | ||
| Section~\ref{sec:background} provides an overview of the data used in this work, the preprocessing techniques, and the machine learning models and techniques employed in our proposed pipeline. | ||
| In Section~\ref{sec:baseline_replica}, we present a brief introduction to the \gls{moc} model and our previous work on replicating it. The results are used as a baseline comparison for our stacking ensemble. |
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Focus in the section is what we've done this semester, not recapping last semester
Suggested change
| In Section~\ref{sec:baseline_replica}, we present a brief introduction to the \gls{moc} model and our previous work on replicating it. The results are used as a baseline comparison for our stacking ensemble. | |
| In Section~\ref{sec:baseline_replica}, we present a brief introduction to the \gls{moc} model and our work on replicating it, focusing on improvements made in this work. The results are used as a baseline comparison for our stacking ensemble. |
| Section~\ref{sec:background} provides an overview of the data used in this work, the preprocessing techniques, and the machine learning models and techniques employed in our proposed pipeline. | ||
| In Section~\ref{sec:baseline_replica}, we present a brief introduction to the \gls{moc} model and our previous work on replicating it. The results are used as a baseline comparison for our stacking ensemble. | ||
| Section~\ref{sec:proposed_approach} introduces our proposed approach to addressing the problem definition, including model and preprocessing selection, data partitioning and cross-validation strategy, and our optimization framework. | ||
| Section~\ref{sec:methodology} details our experimental design and presents the results of the experiments conducted in this work, including those of our stacking ensemble. |
Pattrigue
approved these changes
Jun 12, 2024
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