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Transactions on Rough Sets XV [electronic resource] / edited by James F. Peters, Andrzej Skowron.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Lecture Notes in Computer Science ; 7255 | Lecture Notes in Computer Science ; 7255Editor: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012Descripción: IX, 181p. 54 illus. online resourceTipo de contenido:
  • text
Tipo de medio:
  • computer
Tipo de soporte:
  • online resource
ISBN:
  • 9783642319037
Trabajos contenidos:
  • SpringerLink (Online service)
Tema(s): Formatos físicos adicionales: Sin títuloClasificación CDD:
  • 006.4 23
Clasificación LoC:
  • Q337.5
  • TK7882.P3
Recursos en línea: Springer eBooksResumen: The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume XV offers a number of research streams that have grown out of the seminal work by Zdzislaw Pawlak. The 4 contributions included in this volume presents a rough set approach in machine learning; the introduction of multi-valued near set theory; the advent of a complete system that supports a rough-near set approach to digital image analysis; and an exhaustive study of the mathematics of vagueness.
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The LNCS journal Transactions on Rough Sets is devoted to the entire spectrum of rough sets related issues, from logical and mathematical foundations, through all aspects of rough set theory and its applications, such as data mining, knowledge discovery, and intelligent information processing, to relations between rough sets and other approaches to uncertainty, vagueness, and incompleteness, such as fuzzy sets and theory of evidence. Volume XV offers a number of research streams that have grown out of the seminal work by Zdzislaw Pawlak. The 4 contributions included in this volume presents a rough set approach in machine learning; the introduction of multi-valued near set theory; the advent of a complete system that supports a rough-near set approach to digital image analysis; and an exhaustive study of the mathematics of vagueness.

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