機器學習在移動計算中的應用–交通狀態辨識的個案研究

Machine Learning Applications in Mobile Computing – A Case Study on Transportation-mode Detection

于桐(Tong Yu)

指導教授 ： 林智仁

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2014年)

https://doi.org/10.6342/NTU.2014.02493

摘要

機器學習技術廣泛地運用在很多領域中,包括一些移動計算應用。目前,這些機器學習技術被當做黑盒使用。也就是說,很多在移動計算領域中的研究人員僅僅將一些現有的機器學習工具箱,很直接地使用在他們的資料集上,最後彙報他們的結果。然而,在不深入瞭解一些機器學習的細節問題的時候,不僅這些獲得的結果跟最優的結果有差距,而且這些結果可能會誤導人們。在這篇碩士論文中,通過個案研究如何將一個基於支撐向量機的交通辨識系統部署在一個低消耗並且有限記憶體的設備上,我們來說明在一些移動應用中正確使用機器學習方法的重要性。

關鍵字

交通狀態 ； 分類 ； 支撐向量機 ； 感測器中樞 ； 情境感知運算

並列摘要

Machine Learning techniques are widely used in many areas including mobile applications. Currently, these techniques are often applied as a black box. That is, most researchers in the mobile area simply get one machine learning package, conveniently run it on their data, and report the result. However, without getting into certain details of the machine learning method, not only is the obtained performance far from the optimum, but also the reported results may be misleading. In this paper, through a case study on porting an SVM-based transportation-mode detector to a low-power and low-memory device, we demonstrate the importance of being careful in applying machine learning methods for mobile applications.

並列關鍵字

Transportation mode ； Classification ； Support vector machine ； Sensor hub ； Context-aware computing

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