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: Inductive Logic Programming
    sub title: 27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers
  name:
    name part: Lachiche, Nicolas.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Vrain, Christel.
    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
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 185 p. 101 illus., 7 illus. in color. online resource.
  abstract: This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Conference on Inductive Logic Programming, ILP 2017, held in Orléans, France, in September 2017. The 12 full papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
  table of contents: Relational Affordance Learning for Task-dependent Robot Grasping -- Positive and Unlabeled Relational Classification Through Label Frequency Estimation -- On Applying Probabilistic Logic Programming to Breast Cancer Data -- Logical Vision: One-Shot Meta-Interpretive Learning from Real Images -- Demystifying Relational Latent Representations -- Parallel Online Learning of Event Definitions -- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach -- Parallel Inductive Logic Programming System for Super-linear Speedup -- Inductive Learning from State Transitions over Continuous Domains -- Stacked Structure Learning for Lifted Relational Neural Networks -- Pruning Hypothesis Spaces Using Learned Domain Theories -- An Investigation into the Role of Domain-knowledge on the Use of Embeddings.
  note: edited by Nicolas Lachiche, Christel Vrain.
  subject:
    topic: Mathematical logic
  subject:
    topic: Artificial intelligence
  subject:
    topic: Programming languages (Electronic computers)
  subject:
    topic: Computer logic
  subject:
    topic: Computer programming
  subject:
    topic: Mathematical Logic and Formal Languages
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Programming Languages, Compilers, Interpreters
  subject:
    topic: Logics and Meanings of Programs
  subject:
    topic: Programming Techniques
  classification: QA8.9-10.3
  classification: 005.131
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Artificial Intelligence ; 10759
  identifier: 9783319780900
  identifier: https://doi.org/10.1007/978-3-319-78090-0
  record info:
    record creation date: 180314
    record change date: 20191028142435.0
    record identifier: 978-3-319-78090-0