Books
|
Buy Now
Machine Learning under Resource Constraints - Applications (Paperback)
Loot Price: R3,404
Discovery Miles 34 040
|
|
Machine Learning under Resource Constraints - Applications (Paperback)
Series: De Gruyter STEM
Expected to ship within 10 - 15 working days
|
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.
General
Imprint: |
De Gruyter
|
Country of origin: |
Germany |
Series: |
De Gruyter STEM |
Release date: |
2023 |
First published: |
2023 |
Editors: |
Katharina Morik
• Jörg Rahnenführer
• Christian Wietfeld
|
Dimensions: |
240 x 170mm (L x W) |
Format: |
Paperback - Paperback (DE)
|
Pages: |
478 |
ISBN-13: |
978-3-11-078597-5 |
Categories: |
Books
|
LSN: |
3-11-078597-8 |
Barcode: |
9783110785975 |
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.