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  • 1
    UID:
    b3kat_BV043858717
    Format: X, 147 Seiten
    ISBN: 9783110480139 , 9783110481075
    Note: Erscheint auch als Open Access bei De Gruyter
    Additional Edition: Erscheint auch als Online-Ausgabe, PDF ISBN 978-3-11-048106-8 10.1515/9783110481068
    Additional Edition: Erscheint auch als Online-Ausgabe, EPUB ISBN 978-3-11-048030-6 10.1515/9783110481068
    Language: English
    Subjects: Computer Science , Mathematics
    RVK:
    RVK:
    Keywords: Lineares Ungleichungssystem ; Graphentheorie ; Kombinatorische Optimierung ; Mustererkennung
    URL: Volltext  (kostenfrei)
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Online Resource
    Online Resource
    Berlin ;Boston :De Gruyter,
    UID:
    almafu_9958354675802883
    Format: 1 online resource (158p.)
    ISBN: 9783110481068
    Content: Data mining and pattern recognition are areas based on the mathematical constructions discussed in this monograph. By using combinatorial and graph theoretical techniques, it is shown how to tackle infeasible systems of linear inequalities. These are, in turn, building blocks of geometric decision rules for pattern recognition.
    Note: Frontmatter -- , Preface -- , Contents -- , 1. Pattern recognition, infeasible systems of linear inequalities, and graphs -- , 2. Complexes, (hyper)graphs, and inequality systems -- , 3. Polytopes, positive bases, and inequality systems -- , 4. Monotone Boolean functions, complexes, graphs, and inequality systems -- , 5. Inequality systems, committees, (hyper)graphs, and alternative covers -- , Bibliography -- , List of notation -- , Index , In English.
    Additional Edition: ISBN 978-3-11-048013-9
    Language: English
    Subjects: Computer Science , Mathematics
    RVK:
    RVK:
    Keywords: Electronic books ; Electronic books. ; Electronic books.
    URL: Volltext  (lizenzpflichtig)
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  • 3
    Online Resource
    Online Resource
    De Gruyter | Berlin, [Germany] ; : De Gruyter,
    UID:
    almahu_9948249612302882
    Format: 1 online resource (x, 147 pages)
    Edition: 1st ed.
    ISBN: 3-11-048030-1 , 3-11-048106-5
    Content: This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition.Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property - systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology.The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions. Contents:PrefacePattern recognition, infeasible systems of linear inequalities, and graphsInfeasible monotone systems of constraintsComplexes, (hyper)graphs, and inequality systemsPolytopes, positive bases, and inequality systemsMonotone Boolean functions, complexes, graphs, and inequality systemsInequality systems, committees, (hyper)graphs, and alternative coversBibliographyList of notationIndex
    Note: Frontmatter -- , Preface -- , Contents -- , 1. Pattern recognition, infeasible systems of linear inequalities, and graphs -- , 2. Complexes, (hyper)graphs, and inequality systems -- , 3. Polytopes, positive bases, and inequality systems -- , 4. Monotone Boolean functions, complexes, graphs, and inequality systems -- , 5. Inequality systems, committees, (hyper)graphs, and alternative covers -- , Bibliography -- , List of notation -- , Index , In English.
    Additional Edition: ISBN 3-11-048013-1
    Language: German
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  • 4
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : De Gruyter
    UID:
    gbv_1778609864
    Format: 1 Online-Ressource
    ISBN: 9783110481068
    Content: This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition.Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property – systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology.The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions
    Note: English
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 5
    Online Resource
    Online Resource
    [Erscheinungsort nicht ermittelbar] : De Gruyter
    UID:
    gbv_177860031X
    Format: 1 Online-Ressource (148 p.)
    ISBN: 9783110481068
    Content: Data mining and pattern recognition are areas based on the mathematical constructions discussed in this monograph. By using combinatorial and graph theoretical techniques, it is shown how to tackle infeasible systems of linear inequalities. These are, in turn, building blocks of geometric decision rules for pattern recognition
    Note: English
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    UID:
    gbv_1777648858
    Format: 1 Online-Ressource (X, 147 Seiten) , Illustrationen
    ISBN: 9783110481068 , 9783110480306
    Note: Literaturverzeichnis Seite 133-140
    Additional Edition: ISBN 3110480131
    Additional Edition: ISBN 9783110480139
    Additional Edition: ISBN 9783110481075
    Additional Edition: ISBN 9783110481075
    Additional Edition: Erscheint auch als Druck-Ausgabe Gainanov, Damir N. Graphs for pattern recognition Berlin : De Gruyter, 2016 ISBN 3110480131
    Additional Edition: ISBN 9783110480139
    Additional Edition: ISBN 9783110481075
    Language: English
    Subjects: Computer Science , Mathematics
    RVK:
    RVK:
    Keywords: Lineares Ungleichungssystem ; Graphentheorie ; Kombinatorische Optimierung ; Mustererkennung
    URL: Cover
    URL: Volltext  (Open Access)
    URL: Cover
    URL: Cover
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  • 7
    Online Resource
    Online Resource
    [s.l.] :De Gruyter,
    UID:
    almahu_9949550240602882
    Format: 1 online resource (1 p.)
    ISBN: 9783110481068
    Content: This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition. Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property - systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology. The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions.
    Language: English
    Keywords: Electronic books.
    URL: Image  (Thumbnail cover image)
    Library Location Call Number Volume/Issue/Year Availability
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  • 8
    Online Resource
    Online Resource
    De Gruyter,
    UID:
    kobvindex_HPB960975717
    Format: 1 online resource (158)
    ISBN: 3110481065 , 9783110481068
    Content: This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition. Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property - systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology. The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions. Contents: Preface Pattern recognition, infeasible systems of linear inequalities, and graphs Infeasible monotone systems of constraints Complexes, (hyper)graphs, and inequality systems Polytopes, positive bases, and inequality systems Monotone Boolean functions, complexes, graphs, and inequality systems Inequality systems, committees, (hyper)graphs, and alternative covers Bibliography List of notation Index.
    Note: Frontmatter -- , Preface -- , Contents -- , 1. Pattern recognition, infeasible systems of linear inequalities, and graphs -- , 2. Complexes, (hyper)graphs, and inequality systems -- , 3. Polytopes, positive bases, and inequality systems -- , 4. Monotone Boolean functions, complexes, graphs, and inequality systems -- , 5. Inequality systems, committees, (hyper)graphs, and alternative covers -- , Bibliography -- , List of notation -- , Index
    Additional Edition: Print version: ISBN 9783110480139
    Additional Edition: ISBN 3110480131
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 9
    Online Resource
    Online Resource
    Berlin ;Boston :De Gruyter,
    UID:
    edocfu_9958354675802883
    Format: 1 online resource (158p.)
    ISBN: 9783110481068
    Content: Data mining and pattern recognition are areas based on the mathematical constructions discussed in this monograph. By using combinatorial and graph theoretical techniques, it is shown how to tackle infeasible systems of linear inequalities. These are, in turn, building blocks of geometric decision rules for pattern recognition.
    Note: Frontmatter -- , Preface -- , Contents -- , 1. Pattern recognition, infeasible systems of linear inequalities, and graphs -- , 2. Complexes, (hyper)graphs, and inequality systems -- , 3. Polytopes, positive bases, and inequality systems -- , 4. Monotone Boolean functions, complexes, graphs, and inequality systems -- , 5. Inequality systems, committees, (hyper)graphs, and alternative covers -- , Bibliography -- , List of notation -- , Index , In English.
    Additional Edition: ISBN 978-3-11-048013-9
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 10
    Online Resource
    Online Resource
    De Gruyter | Berlin, [Germany] ; : De Gruyter,
    UID:
    edocfu_9959237949302883
    Format: 1 online resource (x, 147 pages)
    Edition: 1st ed.
    ISBN: 3-11-048030-1 , 3-11-048106-5
    Content: This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition.Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property - systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology.The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions. Contents:PrefacePattern recognition, infeasible systems of linear inequalities, and graphsInfeasible monotone systems of constraintsComplexes, (hyper)graphs, and inequality systemsPolytopes, positive bases, and inequality systemsMonotone Boolean functions, complexes, graphs, and inequality systemsInequality systems, committees, (hyper)graphs, and alternative coversBibliographyList of notationIndex
    Note: Frontmatter -- , Preface -- , Contents -- , 1. Pattern recognition, infeasible systems of linear inequalities, and graphs -- , 2. Complexes, (hyper)graphs, and inequality systems -- , 3. Polytopes, positive bases, and inequality systems -- , 4. Monotone Boolean functions, complexes, graphs, and inequality systems -- , 5. Inequality systems, committees, (hyper)graphs, and alternative covers -- , Bibliography -- , List of notation -- , Index , In English.
    Additional Edition: ISBN 3-11-048013-1
    Language: German
    Library Location Call Number Volume/Issue/Year Availability
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