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: Topics in Grammatical Inference
  name:
    name part: Heinz, Jeffrey.
    role:
      role term: editor.
  name:
    name part: Sempere, José M.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVII, 247 p. 56 illus., 7 illus. in color. online resource.
  abstract: This book explains advanced theoretical and application-related issues in grammatical inference, a research area inside the inductive inference paradigm for machine learning. The first three chapters of the book deal with issues regarding theoretical learning frameworks; the next four chapters focus on the main classes of formal languages according to Chomsky's hierarchy, in particular regular and context-free languages; and the final chapter addresses the processing of biosequences. The topics chosen are of foundational interest with relatively mature and established results, algorithms and conclusions. The book will be of value to researchers and graduate students in areas such as theoretical computer science, machine learning, computational linguistics, bioinformatics, and cognitive psychology who are engaged with the study of learning, especially of the structure underlying the concept to be learned. Some knowledge of mathematics and theoretical computer science, including formal language theory, automata theory, formal grammars, and algorithmics, is a prerequisite for reading this book.
  table of contents: Introduction -- Gold-Style Learning Theory -- Efficiency in the Identification in the Limit Learning Paradigm -- Learning Grammars and Automata with Queries -- On the Inference of Finite State Automata from Positive and Negative Data -- Learning Probability Distributions Generated by Finite-State Machines -- Distributional Learning of Context-Free and Multiple -- Context-Free Grammars -- Learning Tree Languages -- Learning the Language of Biological Sequences.
  note: edited by Jeffrey Heinz, José M. Sempere.
  subject:
    topic: Computer science
  subject:
    topic: Computers
  subject:
    topic: Artificial intelligence
  subject:
    topic: Bioinformatics
  subject:
    topic: Computational linguistics
  subject:
    topic: Computer Science
  subject:
    topic: Theory of Computation
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Computational Linguistics
  subject:
    topic: Computational Biology/Bioinformatics
  classification: QA75.5-76.95
  classification: 004.0151
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783662483954
  identifier: http://dx.doi.org/10.1007/978-3-662-48395-4
  record info:
    record creation date: 160504
    record change date: 20160504142232.0
    record identifier: 978-3-662-48395-4