Rafa Scherer (29 results)
Language: English
Published by Springer 2019
Series: Studies in Computational Intelligence, Book 326 of 538. Book 326 of 538 - Studies in Computational Intelligence
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Thetwo-volume set LNAI 13588 and 13589 constitutes the refereed post-conference proceedings of the 21st International Conference on Artificial Intelligence and Soft Computing, ICAISC 2022, held in Zakopane, Poland, during June 19-23, 2022.The 69 re…vised full papers presented in these proceedings were carefully reviewed andselected from 161 submissions. The papers are organized in the following topicalsections:Volume I:Neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; pattern classification; artificial intelligence in modeling and simulation.Volume II:Computer vision, image and speech analysis; data mining; various problems of artificial intelligence; bioinformatics, biometrics and medical applications.
Language: English
Published by Springer 2020
Series: Studies in Computational Intelligence, Book 326 of 538. Book 326 of 538 - Studies in Computational Intelligence
- Softcover
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Taschenbuch. Condition: Neu. Computer Vision Methods for Fast Image Classi¿cation and Retrieval | Rafa¿ Scherer | Taschenbuch | ix | Englisch | 2020 | Springer | EAN 9783030121976 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: p…reigu.
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Taschenbuch. Condition: Neu. Multiple Fuzzy Classification Systems | Rafa¿ Scherer | Taschenbuch | Studies in Fuzziness and Soft Computing | xii | Englisch | 2014 | Springer | EAN 9783642436574 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com… | Anbieter: preigu.
Language: English
Published by Springer 2019
Series: Studies in Computational Intelligence, Book 326 of 538. Book 326 of 538 - Studies in Computational Intelligence
- Hardcover
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Language: English
Published by Springer, Springer 2020
Series: Studies in Computational Intelligence, Book 326 of 538. Book 326 of 538 - Studies in Computational Intelligence
- Softcover
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as 'hand-crafted features.' It introduces readers to novel rapid image description methods based on loc…al and global features, as well as several techniques for comparing images.Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book's main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Fuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statistical c…lassification. Rough set theory is useful when data sets are incomplete. It defines a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classification. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a finite set of learning models, usually weak learners. The present book discusses the three aforementioned fields - fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classification ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. .
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Fuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statis…tical classification. Rough set theory is useful when data sets are incomplete. It defines a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classification. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a finite set of learning models, usually weak learners. The present book discusses the three aforementioned fields - fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classification ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. .
Language: English
Published by Springer 2019
Series: Studies in Computational Intelligence, Book 326 of 538. Book 326 of 538 - Studies in Computational Intelligence
- Hardcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as 'hand-crafted features.' It introduces readers to novel rapid image description methods based on local and…global features, as well as several techniques for comparing images.Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book's main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereedproceedings of the 16th International Conference on ArtificialIntelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers pres…ented were carefully reviewed and selected from 274 submissions. The papers included in the first volumeare organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; computer vision, image and speech analysis; and bioinformatics, biometrics and medical applications.
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Language: English
Published by Springer Berlin Heidelberg, Springer Berlin Heidelberg Jun 2012 2012
- Hardcover
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Fuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common…to statistical classification. Rough set theory is useful when data sets are incomplete. It defines a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classification. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a finite set of learning models, usually weak learners. The present book discusses the three aforementioned fields - fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classification ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. . 144 pp. Englisch.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Fuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions…common to statistical classification. Rough set theory is useful when data sets are incomplete. It defines a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classification. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a finite set of learning models, usually weak learners. The present book discusses the three aforementioned fields - fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classification ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. . 144 pp. Englisch.
Language: English
Published by Springer International Publishing Feb 2019 2019
Series: Studies in Computational Intelligence, Book 326 of 538. Book 326 of 538 - Studies in Computational Intelligence
- Hardcover
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermanyBuchWeltWeit Ludwig Meier e.K.
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as 'hand-crafted features.' It introduces readers to novel rapid image description methods bas…ed on local and global features, as well as several techniques for comparing images.Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book's main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions. 148 pp. Englisch.
Language: English
Published by Springer International Publishing Okt 2020 2020
Series: Studies in Computational Intelligence, Book 326 of 538. Book 326 of 538 - Studies in Computational Intelligence
- Softcover
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as 'hand-crafted features.' It introduces readers to novel rapid image description meth…ods based on local and global features, as well as several techniques for comparing images.Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book's main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions. 148 pp. Englisch.
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Condition: New. Print on Demand pp. 144 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.
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Condition: New. Print on Demand pp. 144 24 Illus.
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Condition: New. PRINT ON DEMAND pp. 144.
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Condition: New. PRINT ON DEMAND pp. 144.
Language: English
Published by Springer, Palgrave Macmillan Okt 2020 2020
Series: Studies in Computational Intelligence, Book 326 of 538. Book 326 of 538 - Studies in Computational Intelligence
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as 'hand-crafted features.' It introduces readers to novel rapid image description methods…based on local and global features, as well as several techniques for comparing images.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 148 pp. Englisch.
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Condition: New. PRINT ON DEMAND pp. 704.
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Fuzzy classi¿ers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scienti¿c and business applications. Fuzzy classi¿ers use fuzzy rules and do not require assumptions common to stat…istical classi¿cation. Rough set theory is useful when data sets are incomplete. It de¿nes a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classi¿cation. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a ¿nite set of learning models, usually weak learners.The present book discusses the three aforementioned ¿elds ¿ fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classi¿cation ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. .Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 144 pp. Englisch.
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Condition: New. PRINT ON DEMAND pp. 852.
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Condition: New. PRINT ON DEMAND pp. 850.
Language: English
Published by Springer, Palgrave Macmillan Feb 2019 2019
Series: Studies in Computational Intelligence, Book 326 of 538. Book 326 of 538 - Studies in Computational Intelligence
- Hardcover
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as 'hand-crafted features.' It introduces readers to novel rapid image description methods based o…n local and global features, as well as several techniques for comparing images.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 148 pp. Englisch.
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Fuzzy classi¿ers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scienti¿c and business applications. Fuzzy classi¿ers use fuzzy rules and do not require assumptions common…to statistical classi¿cation. Rough set theory is useful when data sets are incomplete. It de¿nes a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classi¿cation. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a ¿nite set of learning models, usually weak learners.The present book discusses the three aforementioned ¿elds ¿ fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classi¿cation ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory. .Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 144 pp. Englisch.
