Semi-Supervised Learning: Predicting Activities in Android Environment
Predicting activities from data gathered with sensors gained importance over the years with the objective of getting a better understanding of the human body. This paper is to show that predicting activities on an Android phone is possible. The authors take into consideration different classifiers, their accuracy using different approaches (hierarchical and one step classification) and limitations of the mobile itself like battery and memory usage. A semi-supervised learning approach is taken in order to compare its results against supervised learning.
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