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  • 1
    Online Resource
    Online Resource
    Cambridge, United Kingdom ; New York, NY, USA ; Port Melbourne, Australia ; New Delhi, India ; Singapore :Cambridge University Press,
    UID:
    almafu_BV047052238
    Format: 1 Online-Ressource (xviii, 689 Seiten).
    ISBN: 978-1-108-64298-9
    Content: Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered
    Note: Title from publisher's bibliographic system (viewed on 12 Nov 2020)
    Additional Edition: Erscheint auch als Druck-Ausgabe, Hardcover ISBN 978-1-108-49370-3
    Language: English
    Subjects: Biology , Psychology
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    Keywords: Maschinelles Lernen ; Statistik ; Neurowissenschaften
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 2
    UID:
    b3kat_BV049594501
    Format: 1 Online-Ressource (XII, 131 p. 59 illus., 47 illus. in color)
    Edition: 1st ed. 2024
    ISBN: 9783031543036
    Series Statement: Communications in Computer and Information Science 2020
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-54302-9
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-54304-3
    Language: English
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 3
    Online Resource
    Online Resource
    Cambridge :Cambridge University Press,
    UID:
    almahu_9948619432502882
    Format: 1 online resource (xviii, 689 pages) : , digital, PDF file(s).
    ISBN: 9781108642989 (ebook)
    Content: Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered.
    Note: Title from publisher's bibliographic system (viewed on 12 Nov 2020).
    Additional Edition: Print version: ISBN 9781108493703
    Language: English
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  • 4
    UID:
    almahu_9949384308102882
    Format: 1 online resource.
    ISBN: 9781351128360 , 1351128361 , 9781351128384 , 1351128388 , 9781351128377 , 135112837X , 9781351128353 , 1351128353
    Series Statement: Data mining and knowledge series
    Content: "This book shows how machine learning can be applied to address real-world problems in the fourth industrial revolution and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society"--
    Note: 1 The Fourth Industrial Revolution 2 Machine Learning 3 Applications of Machine Learning in Industrial Sectors 4 Component-Level Case Study: Remaining Useful Life of Bearings 5 Machine-Level Case Study: Fingerprint of Industrial Motors 6 Production-Level Case Study: Automated Visual Inspection of a Laser Process 7 Distribution-Level Case Study: Forecasting of Air Freight Delays.
    Additional Edition: Print version: Industrial applications of machine learning Boca Raton, Florida : CRC Press, [2019] ISBN 9780815356226 (hardback : alk. paper)
    Language: English
    Keywords: Electronic books. ; Electronic books.
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  • 5
    UID:
    gbv_467803129
    Format: IX, 276 S
    Language: Spanish
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    UID:
    gbv_338112049
    Format: XXXI, 382 S , graph. Darst , 25 cm
    ISBN: 0792374665
    Series Statement: Genetic algorithms and evolutionary computation 2
    Note: Literaturangaben
    Language: English
    Keywords: Evolutionäre Programmierung ; Aufsatzsammlung
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  • 7
    UID:
    gbv_749178183
    Format: Online-Ressource (XXXIV, 382 p) , digital
    Edition: Springer eBook Collection. Computer Science
    ISBN: 9781461515395
    Series Statement: Genetic Algorithms and Evolutionary Computation 2
    Content: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. `.. I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana
    Additional Edition: ISBN 9781461356042
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9780792374664
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781461356042
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781461515401
    Language: English
    URL: Volltext  (lizenzpflichtig)
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  • 8
    Book
    Book
    New York : Springer Science + Business Media, LLC
    UID:
    b3kat_BV047883258
    Format: xxxi, 382 Seiten , Diagramme
    ISBN: 9780792374664
    Series Statement: Genetic algorithms and evolutionary computation 2
    Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-1-4615-1539-5
    Language: English
    Subjects: Computer Science
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    Keywords: Verteilter Algorithmus ; Aufsatzsammlung
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  • 9
    Book
    Book
    Cambridge, United Kingdom ; New York, NY :Cambridge University Press,
    UID:
    almahu_BV047104011
    Format: xviii, 689 Seiten : , Illustrationen, Diagramme.
    ISBN: 978-1-108-49370-3
    Content: "Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered"--
    Note: Includes bibliographical references and index
    Language: English
    Subjects: Biology , Psychology
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    Keywords: Maschinelles Lernen ; Statistik ; Neurowissenschaften
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  • 10
    UID:
    almahu_9948621421702882
    Format: XXXIV, 382 p. , online resource.
    Edition: 1st ed. 2002.
    ISBN: 9781461515395
    Series Statement: Genetic Algorithms and Evolutionary Computation, 2
    Content: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. `... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana.
    Note: I Foundations -- 1 An Introduction to Evolutionary Algorithms -- 2 An Introduction to Probabilistic Graphical Models -- 3 A Review on Estimation of Distribution Algorithms -- 4 Benefits of Data Clustering in Multimodal Function Optimization via EDAs -- 5 Parallel Estimation of Distribution Algorithms -- 6 Mathematical Modeling of Discrete Estimation of Distribution Algorithms -- II Optimization -- 7 An Empirical Comparison of Discrete Estimation of Distribution Algorithms -- 8 Results in Function Optimization with EDAs in Continuous Domain -- 9 Solving the 0-1 Knapsack Problem with EDAs -- 10 Solving the Traveling Salesman Problem with EDAs -- 11 EDAs Applied to the Job Shop Scheduling Problem -- 12 Solving Graph Matching with EDAs Using a Permutation-Based Representation -- III Machine Learning -- 13 Feature Subset Selection by Estimation of Distribution Algorithms -- 14 Feature Weighting for Nearest Neighbor by EDAs -- 15 Rule Induction by Estimation of Distribution Algorithms -- 16 Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs -- 17 Comparing K-Means, GAs and EDAs in Partitional Clustering -- 18 Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms.
    In: Springer Nature eBook
    Additional Edition: Printed edition: ISBN 9780792374664
    Additional Edition: Printed edition: ISBN 9781461356042
    Additional Edition: Printed edition: ISBN 9781461515401
    Language: English
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