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: Computational Learning Theory
    sub title: Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings
  name:
    name part: Ben-David, Shai.
    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: 1997
    edition: 1st ed. 1997.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: CCCXLVIII, 338 p. online resource.
  abstract: This book constitutes the refereed proceedings of the Third European Conference on Computational Learning Theory, EuroCOLT'97, held in Jerusalem, Israel, in March 1997. The book presents 25 revised full papers carefully selected from a total of 36 high-quality submissions. The volume spans the whole spectrum of computational learning theory, with a certain emphasis on mathematical models of machine learning. Among the topics addressed are machine learning, neural nets, statistics, inductive inference, computational complexity, information theory, and theoretical physics.
  table of contents: Sample compression, learnability, and the Vapnik-Chervonenkis dimension -- Learning boxes in high dimension -- Learning monotone term decision lists -- Learning matrix functions over rings -- Learning from incomplete boundary queries using split graphs and hypergraphs -- Generalization of the PAC-model for learning with partial information -- Monotonic and dual-monotonic probabilistic language learning of indexed families with high probability -- Closedness properties in team learning of recursive functions -- Structural measures for games and process control in the branch learning model -- Learning under persistent drift -- Randomized hypotheses and minimum disagreement hypotheses for learning with noise -- Learning when to trust which experts -- On learning branching programs and small depth circuits -- Learning nearly monotone k-term DNF -- Optimal attribute-efficient learning of disjunction, parity, and threshold functions -- learning pattern languages using queries -- On fast and simple algorithms for finding Maximal subarrays and applications in learning theory -- A minimax lower bound for empirical quantizer design -- Vapnik-Chervonenkis dimension of recurrent neural networks -- Linear Algebraic proofs of VC-Dimension based inequalities -- A result relating convex n-widths to covering numbers with some applications to neural networks -- Confidence estimates of classification accuracy on new examples -- Learning formulae from elementary facts -- Control structures in hypothesis spaces: The influence on learning -- Ordinal mind change complexity of language identification -- Robust learning with infinite additional information.
  note: edited by Shai Ben-David.
  subject:
    topic: Artificial intelligence
  subject:
    topic: Mathematical logic
  subject:
    topic: Computers
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Mathematical Logic and Formal Languages
  subject:
    topic: Computation by Abstract Devices
  classification: Q334-342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Lecture Notes in Artificial Intelligence ; 1208
  identifier: 9783540684312
  identifier: https://doi.org/10.1007/3-540-62685-9
  record info:
    record creation date: 121227
    record change date: 20191022053555.0
    record identifier: 978-3-540-68431-2