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: Data Mining Applications Using Artificial Adaptive Systems
  name:
    name part: Tastle, William J.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VII, 275 p. 135 illus., 111 illus. in color. online resource.
  abstract: This volume directly addresses the complexities involved in data mining and the development of new algorithms, built on an underlying theory consisting of linear and non-linear dynamics, data selection, filtering, and analysis, while including analytical projection and prediction. The results derived from the analysis are then further manipulated such that a visual representation is derived with an accompanying analysis. The book brings very current methods of analysis to the forefront of the discipline, provides researchers and practitioners the mathematical underpinning of the algorithms, and the non-specialist with a visual representation such that a valid understanding of the meaning of the adaptive system can be attained with careful attention to the visual representation. The book presents, as a collection of documents, sophisticated and meaningful methods that can be immediately understood and applied to various other disciplines of research. The content is composed of chapters addressing: An application of adaptive systems methodology in the field of post-radiation treatment involving brain volume differences in children; A new adaptive system for computer-aided diagnosis of the characterization of lung nodules; A new method of multi-dimensional scaling with minimal loss of information; A description of the semantics of point spaces with an application on the analysis of terrorist attacks in Afghanistan; The description of a new family of meta-classifiers; A new method of optimal informational sorting; A general method for the unsupervised adaptive classification for learning; and the presentation of two new theories, one in target diffusion and the other in twisting theory.
  table of contents: Assessing Post-Radiotherapy Treatment involving Brain Volume Differences in Children -- J-Net: an Adaptive System for Computer-Aided Diagnosis in Lung Nodule Characterization -- Population Algorithm: A New Method of Multi-Dimensional Scaling -- Semantics of Point Spaces through the Topological Weighted Centroid and Other Mathematical Quantities -- Meta Net: A New Meta-Classifier Family -- Optimal Informational Sorting: The ACS-ULA Approach -- GUACAMOLE: A New Paradigm for Unsupervised Competitive Learning -- Spatiotemporal Mining: A Systematic Approach to Discrete Diffusion Models for Time and Space Extrapolation.
  note: edited by William J. Tastle.
  subject:
    topic: Computer science
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Data mining
  subject:
    topic: Artificial intelligence
  subject:
    topic: Biometrics (Biology)
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Data Structures
  subject:
    topic: Biometrics
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781461442233
  identifier: http://dx.doi.org/10.1007/978-1-4614-4223-3
  record info:
    record creation date: 120824
    record change date: 20151125191839.0
    record identifier: 978-1-4614-4223-3