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: Encyclopedia of Machine Learning and Data Mining
  name:
    name part: Sammut, Claude.
    role:
      role term: editor.
  name:
    name part: Webb, Geoffrey I.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: 263 illus., 83 illus. in color. eReference. online resource.
  abstract: This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic. Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature. The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.
  table of contents: Abduction -- Adaptive Resonance Theory -- Anomaly Detection -- Bayes Rule -- Case-Based Reasoning -- Categorical Data Clustering -- Causality -- Clustering from Data Streams -- Complexity in Adaptive Systems -- Complexity of Inductive Inference -- Computational Complexity of Learning -- Confusion Matrix -- Connections Between Inductive Inference and Machine Learning -- Covariance Matrix -- Decision List -- Decision Lists and Decision Trees -- Decision Tree -- Deep Learning -- Density-Based Clustering -- Dimensionality Reduction -- Document Classification -- Dynamic Memory Model -- Empirical Risk Minimization -- Error Rate -- Event Extraction from Media Texts -- Evolutionary Clustering -- Evolutionary Computation in Economics -- Evolutionary Computation in Finance -- Evolutionary Computational Techniques in Marketing -- Evolutionary Feature Selection and Construction -- Evolutionary Kernel Learning -- Evolutionary Robotics -- Expectation Maximization Clustering -- Expectation Propagation -- Feature Construction in Text Mining -- Feature Selection -- Feature Selection in Text Mining -- Gaussian Distribution -- Gaussian Process -- Generative and Discriminative Learning -- Grammatical Inference -- Graphical Models -- Hidden Markov Models -- Inductive Inference -- Inductive Logic Programming -- Inductive Programming -- Inductive Transfer -- Inverse Reinforcement Learning -- Kernel Methods -- K-Means Clustering -- K-Medoids Clustering -- K-Way Spectral Clustering -- Learning Algorithm Evaluation -- Learning Graphical Models -- Learning Models of Biological Sequences -- Learning to Rank -- Learning Using Privileged Information -- Linear Discriminant -- Linear Regression -- Locally Weighted Regression for Control -- Machine Learning and Game Playing -- Manhattan Distance -- Maximum Entropy Models for Natural Language Processing -- Mean Shift -- Metalearning -- Minimum Description Length Principle -- Minimum Message Length -- Mixture Model -- Model Evaluation -- Model Trees …
  note: edited by Claude Sammut, Geoffrey I. Webb.
  subject:
    topic: Computer science
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Pattern recognition
  subject:
    topic: Statistics
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Statistics and Computing/Statistics Programs
  subject:
    topic: Pattern Recognition
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781489976871
  identifier: http://dx.doi.org/10.1007/978-1-4899-7687-1
  record info:
    record creation date: 170413
    record change date: 20170413144524.0
    record identifier: 978-1-4899-7687-1