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Machine Learning under Resource Constraints addresses novel machine
learning algorithms that are challenged by high-throughput data, by
high dimensions, or by complex structures of the data in three
volumes. Resource constraints are given by the relation between the
demands for processing the data and the capacity of the computing
machinery. The resources are runtime, memory, communication, and
energy. Hence, modern computer architectures play a significant
role. Novel machine learning algorithms are optimized with regard
to minimal resource consumption. Moreover, learned predictions are
executed on diverse architectures to save resources. It provides a
comprehensive overview of the novel approaches to machine learning
research that consider resource constraints, as well as the
application of the described methods in various domains of science
and engineering. Volume 3 describes how the resource-aware machine
learning methods and techniques are used to successfully solve
real-world problems. The book provides numerous specific
application examples. In the areas of health and medicine, it is
demonstrated how machine learning can improve risk modelling,
diagnosis, and treatment selection for diseases. Machine learning
supported quality control during the manufacturing process in a
factory allows to reduce material and energy cost and save testing
times is shown by the diverse real-time applications in electronics
and steel production as well as milling. Additional application
examples show, how machine-learning can make traffic, logistics and
smart cities more effi cient and sustainable. Finally, mobile
communications can benefi t substantially from machine learning,
for example by uncovering hidden characteristics of the wireless
channel.
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