LEADER 02498nam 2200385 450 001 9910645947203321 005 20230510102237.0 024 7 $a10.1515/9783110785944 035 $a(CKB)5860000000285453 035 $a(NjHacI)995860000000285453 035 $a(EXLCZ)995860000000285453 100 $a20230510d2022 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMachine learning under resource constraints$hVolume 1$iFundamentals /$fedited by Katharina Morik and Peter Marwedel 210 1$aBerlin ;$aBoston :$cDe Gruyter,$d2022. 215 $a1 online resource (xi, 489 pages) $cillustrations 225 1 $aDe Gruyter STEM 311 $a3-11-078612-5 330 $aMachine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters. 410 0$aDe Gruyter STEM. 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 702 $aMarwedel$b Peter 702 $aMorik$b Katharina 801 0$bNjHacI 801 1$bNjHacl 906 $aBOOK 912 $a9910645947203321 996 $aMachine Learning under Resource Constraints$93011774 997 $aUNINA