Page 35 - Artificial Intelligence for the Internet of Everything

22 Artificial Intelligence for the Internet of Everything

Fig. 2.1 Diagram of drone flight over a roadway.

This example scenario highlights several research issues specific to IoBT
settings, as reflected in prior surveys (e.g., Suri et al., 2016; Zheng & Carter,
2015): (i) a needed capability to quickly gather training data reflecting
unforeseen learning/classification tasks; (ii) a needed capability to incremen-
tally learn over the stream of field-specific data (e.g., increasing the accuracy
of classifying VBIEDs by learning over the stream of pictures of decorated
cars collected over 10 minutes of flight time); and (iii) management of lim-
ited network bandwidth and connectivity between assets (e.g., between the
UAS and UGS along the road) requiring selective asset use to obtain
classifier-relevant data that increases the classifier knowledge.
Each of these issues requires the selection of learning and classification
methods appropriate to stream-based data sources. Prior research (Bottou,
1998 b; Bottou & Cun, 2004; Vapnik, 2013) demonstrates the equivalence
of learning from stream-based data in real time with learning from infinite
samples. From this work it follows that statistical-learning methods adept
to large-scale data sources may be applicable for stream-based data.
This chapter opens with a survey of classical and modern statistical-
learning theory, and how numerical optimization can be used to solve
the resulting mathematical problems. The objective of this chapter is to
encourage the IoT and ML research communities to revisit the underlying
mathematical underpinnings of stream-based learning, as they apply to
IoBT-based systems.

2.3 OPTIMIZATION IN MACHINE LEARNING

Behind the scenes of any ML algorithm is an optimization problem. To
maximize a likelihood or a posterior distribution, or minimize a loss func-
tion, one must rely on mathematical optimization.