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 and Knowledge Discovery in Databases
    sub title: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part III
  name:
    name part: Hutter, Frank.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Kersting, Kristian.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Lijffijt, Jefrey.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Valera, Isabel.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2021
    edition: 1st ed. 2021.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XLIII, 755 pages 236 illustrations, 213 illustrations in color.)
  abstract: The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track. .
  table of contents: Combinatorial optimization -- large-scale optimization and differential privacy -- boosting and ensemble methods -- Bayesian methods -- architecture of neural networks -- graph neural networks -- Gaussian processes -- computer vision and image processing -- natural language processing.-bioinformatics.
  note: edited by Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera.
  subject:
    topic: Data mining
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Artificial intelligence
  subject:
    topic: Numerical analysis
  subject:
    topic: Application software
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Data Structures and Information Theory
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Numeric Computing
  subject:
    topic: Computer Appl. in Social and Behavioral Sciences
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Lecture Notes in Artificial Intelligence ; 12459
  identifier: 9783030676643
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 210224
    record change date: 20230330184922.7
    record identifier: in00004391889