Advanced information processing system (AIPS)
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Advanced information processing system (AIPS) proof-of-concept system, functional requirements, I/O network system services

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Published by Charles Stark Draper Laboratory, National Technical Information Service, distributor in Cambridge, Mass, [Springfield, Va .
Written in English

Subjects:

  • Computer architecture,
  • Computer software -- Development,
  • Information networks,
  • Input-output analysis -- Computer programs

Book details:

Edition Notes

Other titlesProof-of-concept system, functional requirements, I/O network system services
SeriesNASA contractor report -- NASA-CR 181481
ContributionsCharles Stark Draper Laboratory, Lyndon B. Johnson Space Center
The Physical Object
FormatMicroform
Paginationix, 32 p
Number of Pages32
ID Numbers
Open LibraryOL14972187M

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Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels () and is a coeditor of Advances in Kernel Methods: Support Vector Learning (), Advances in Large-Margin Classifiers (), and Kernel Methods in Computational Biology (), all published by the MIT Press.   Discovering Information Systems An Exploratory Approach. Post date: 22 Aug An excellent introductory information systems text. Covers the fundamental scientific concepts on which IS builds, an overview of relevant technology and the development and deployment of information systems as well as some wider societal concerns. data capture, processing and distribution. In the cartographic domain, advances in computer hardware and mapping software have already encouraged many statistical and census offices to move from traditional cartographic methods to digital mapping and geographic information systems (GIS) (see, e.g., Rhind, ; Ben-. The past decade has seen greatly increased interaction between theoretical work in neuroscience, cognitive science and information processing, and experimental work requiring sophisticated computational modeling. The contributions in NIPS 8 focus on a wide variety of algorithms and architectures for both supervised and unsupervised learning. They are divided into nine parts: .