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Transactions on Rough Sets XV [electronic resource] /

Contributor(s): Peters, James F [editor.] | Skowron, Andrzej [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science: 7255Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.Description: IX, 181 p. 54 illus. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642319037.Subject(s): Computer science | Mathematical logic | Numerical analysis | Information storage and retrieval | Artificial intelligence | Image processing | Pattern recognition | Computer Science | Pattern Recognition | Artificial Intelligence (incl. Robotics) | Mathematical Logic and Formal Languages | Numeric Computing | Image Processing and Computer Vision | Information Storage and RetrievalOnline resources: Click here to access online In: Springer eBooksSummary: 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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