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
    sub title: a constraint-based approach
  name:
    name part: Gori, Marco
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: ScienceDirect (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: ne
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: Annotation Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines.The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book.This book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included.Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified mannerProvides in-depth coverage of unsupervised and semi-supervised learningIncludes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learningContains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex.
  note: Marco Gori.
  note: The Big Picture Learning Principles Linear-Threshold Machines Kernel Machines Deep Architectures Learning and Reasoning with Constraints Epilogue Answers to selected exercises Appendices: Constrained optimization in Finite Dimensions Regularization operators Calculus of variations Index to Notations.
  subject:
    topic: Machine learning
  subject:
    topic: Algorithms
  subject:
    topic: Algorithms
  subject:
    topic: Machine Learning
  subject:
    topic: Apprentissage automatique
  subject:
    topic: Algorithmes
  subject:
    topic: algorithms
  subject:
    topic: Algorithms
  subject:
    topic: Machine learning
  classification: Q325.5
  classification: 006.3/1
  identifier: 9780081006702
  identifier: 0081006705
  identifier: 9780081006597
  identifier: 0081006594
  identifier: 9780081006702 Ingram Content Group
  identifier: 9780081006702 O'Reilly Media
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NLE
    record creation date: 170926
    record change date: 20260327152825.3
    record identifier: in00005762756
    language of cataloging:
      language term: eng