rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine Learning under Resource Constraints
    part number: Volume 3
    part name: Machine Learning under Resource Constraints - Applications
  name:
    name part: Morik, Katharina
    role:
      role term: editor.
  name:
    name part: Rahnenführer, Jörg
    role:
      role term: editor.
  name:
    name part: Wietfeld, Christian
    role:
      role term: editor.
  name:
    name part: Walter de Gruyter & Co
  type of resource: text
  genre: dictionary
  genre: Electronic books.
  origin info:
    place:
      place term: gw
    place:
      place term: DE
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (VIII, 470 pages).
  abstract: 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.
  table of contents: Frontmatter -- Contents -- 1 Editorial / Katharina Morik, Jörg Rahnenführer, Christian Wietfeld -- 2 Health / Medicine -- 2.1 Machine Learning in Medicine / Catherine Jutzeler, Karsten Borgwardt -- 2.2 Virus Detection / Roland Hergenröder, Frank Weichert, Konstantin Wüstefeld, Victoria Shpacovitch -- 2.3 Cancer Diagnostics and Therapy from Molecular Data / Sven Rahmann, Alexander Schramm, Johannes Köster -- 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction / Zeyu Ding, Katja Ickstadt, Alexander Munteanu -- 2.5 Survival Prediction and Model Selection / Jörg Rahnenführer, Michel Lang, Jakob Richter -- 2.6 Protein Complex Similarity / Bianca K Stöcker, Till Schäfer, Petra Mutzel, Johannes Köster, Nils Kriege, Sven Rahmann -- 3 Industry 4.0 -- 3.1 Keynote on Industry 4.0 / Michael ten Hompel, Moritz Roidl -- 3.2 Quality Assurance in Interlinked Manufacturing Processes / Jochen Deuse, Katharina Morik, Amal Saadallah, Jan Büscher, Thorben Panusch -- 3.3 Label Proportion Learning / Marco Stolpe, Katharina Morik -- 3.4 Simulation and Machine Learning / Petra Wiederkehr, Katharina Morik, Amal Saadallah, Felix Finkeldey -- 3.5 High-Precision Wireless Localization / Janis Tiemann -- 3.6 Indoor Photovoltaic Energy Harvesting / Mojtaba Masoudinejad -- 3.7 Micro-UAV Swarm Testbed for Indoor Applications / Nils Gramse, Moritz Roidl, Shrutarv Awasthi, Christopher Reining -- 4 Smart City and Traffic -- 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors / Thomas Liebig -- 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows / Marcus Haferkamp, Benjamin Sliwa, Christian Wietfeld -- 4.3 Green Networking and Resource Constrained Clients for Smart Cities / Pascal Jörke, Christian Wietfeld -- 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing / Cedrik Krieger, Benjamin Sliwa, Christian Wietfeld -- 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata / Michael Schreckenberg, Tim Vranken …
  target audience: specialized
  note: Katharina Morik, Jörg Rahnenführer, Christian Wietfeld.
  note: Katharina Morik, Christian Wietfeld, Jörg Rahnenführer, TU Dortmund, Germany.
  note: In English.
  subject:
    topic: COM018000 COMPUTERS / Data Processing
  subject:
    topic: COMPUTERS / Database Management / Data Mining
  subject:
    topic: COMPUTERS / Information Technology
  subject:
    topic: COMPUTERS / Programming / Algorithms
  subject:
    topic: SCIENCE / Chemistry / General
  subject:
    topic: Künstliche Intelligenz
  subject:
    topic: Maschinelles Lernen
  subject:
    topic: Eingebettete Systeme
  subject:
    topic: Big Data
  subject:
    topic: Resource-Constrained Data Analysis
  subject:
    topic: Resource-Aware Machine Learning
  subject:
    topic: Embedded Systems and Machine Learning
  subject:
    topic: Big Data and Machine Learning
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Highly Distributed Data
  subject:
    topic: ML on Small devices
  subject:
    topic: Data mining for Ubiquitous System Software
  subject:
    topic: Cyber-physical systems
  subject:
    topic: Machine learning in high-energy physics
  subject:
    topic: Machine learning for knowledge discovery
  related item:
    title info:
      title: De Gruyter STEM
  identifier: 9783110785982
  identifier: 3110785986
  identifier: https://www.degruyterbrill.com/openurl
  record info:
    record content source: DEGRU
    record creation date: 250920
    record change date: 20260212200958.0
    record identifier: in00005687311
    language of cataloging:
      language term: eng