Supervised Learning Techniques in Mobile Device Apps for Androids

Abstract

Mobile devices have become an integral part of our daily lives. Most people carry smartphones today almost everywhere; and have other mobile devices such as tablets, often more convenient than full-fledged laptops for work transit, short trips etc. This had led to development of apps for mobile devices, easy to download and access anywhere anytime. An important field improving human experiences on mobile devices is machine learning. This constitutes technqiues involving acquisition of knowledge, skills and understanding by machines from examples, guidance, experience or reflection to learn analogous to humans. Among learning paradigms herein, supervised learning comprises situations where labeled training samples are provided to administer the process, making it more regulated, similar to human instructors providing such examples with notions of correctness to guide human learners. Supervised learning techniques are useful in designing mobile apps as they entail guided examples capturing specific human needs and their reasoning in activities, e.g., classification. This paper gives a comprehensive review of a few useful supervised learning approaches along with their implementation in mobile apps, focusing on Androids as they constitute over 50% of the global smartphone market. It includes description of the approaches and portrays interesting Android apps deploying them, addressing classification and regression problems. We discuss the contributions and critiques of the apps and also present open issues with the potential for further research in related areas. This paper is expected to be useful to students, researchers and developers in mobile computing, human computer interaction, data mining and machine learning.