數據整合與數學運算：實現不同精度數據的無縫融合

Abstract

本論文針對研究連續性但不同精度的數據做無縫融合，其主要是透過數據整合和數學運算技術實現了不同精度數據之間的整合，並與現有常見的方法比較，例如內插法、Cubic等，對於其運算時間、運算效果、套用模型作相對比較，本篇以主成分分析(Principal components analysis，PCA)配合支援向量機(Support Vector Machine，SVM)為例子，設計在不同情境下的模型運算與套用，是如何從而提高數據整合和分析的效率及精確度。在本研究中，我們提出的方法能夠有效地提高數據整合的精確度與減少運算時間，並且能夠適用於各種不同的數據精度。透過實驗我們發現該方法能夠有效的提高數據整合的精確度與減少運算時間，相較於其他常見方法，我們所提出的方法在各個方面都取得了更好的效果，並且能夠適用於各種不同的數據精度，本論文可以應用於各種領域，例如機械學習、大數據分析、主成分分析等，提高數據處理的效率和精確度。

This paper focuses on seamless integration of data with varying accuracies, specifically through data fusion and mathematical computation techniques. A comparison is made with existing common methods such as interpolation, Cubic, etc., in terms of computation time, computational effectiveness, and application to models. This paper uses Principal Component Analysis (PCA) combined with Support Vector Machine (SVM) as an example to design model computation and application in different scenarios, demonstrating how it improves the efficiency and accuracy of data integration and analysis.In this study, the proposed method effectively enhances the accuracy of data integration and reduces computation time, while being adaptable to various data accuracies. Through experiments, we have found that this method significantly improves the accuracy of data integration and reduces computation time compared to other common methods. It demonstrates superior performance in various aspects and is applicable to different data accuracies. This paper can be applied in various fields such as machine learning, big data analysis, and principal component analysis, thereby enhancing the efficiency and accuracy of data processing.