UID:
kobvindex_HPB1356994849
Format:
1 online resource (XIV, 491 p.).
ISBN:
9783110785944
,
3110785943
Series Statement:
De Gruyter STEM Ser.
Content:
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 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.
Note:
Frontmatter --
,
Contents --
,
Preface --
,
1 Introduction --
,
Data Gathering and Resource Measuring --
,
3 Streaming Data, Small Devices --
,
4 Structured Data --
,
5 Cluster Analysis --
,
6 Hardware-Aware Execution --
,
7 Memory Awareness --
,
8 Communication Awareness --
,
9 Energy Awareness --
,
Bibliography --
,
Index --
,
List of Contributors
,
In English.
Additional Edition:
ISBN 9783110786125
Additional Edition:
ISBN 9783110785937
Language:
English
DOI:
10.1515/9783110785944
URL:
https://doi.org/10.1515/9783110785944
URL:
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