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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:
    almahu_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:
    almafu_9960118422302883
    Format: 1 online resource (xviii, 689 pages) : , digital, PDF file(s).
    ISBN: 1-108-63904-6 , 1-108-64298-5
    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: ISBN 1-108-49370-X
    Language: English
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  • 4
    UID:
    gbv_467803129
    Format: IX, 276 S
    Language: Spanish
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  • 5
    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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  • 6
    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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  • 7
    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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  • 8
    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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  • 9
    UID:
    kobvindex_ZLB13557252
    Format: 1 DVD Video (ca. 58 Min.) : s/w , Tonformat: DD/2.0 Stereo (Stummfilm) , PAL , Bildformat: 1.33:1 Orig.
    Series Statement: Coleccion FilmotecaEspañola : [DVD Video]
    Content: En una mísera aldea castellana malviven el labrador Juan Castilla (Pedro Larrañaga), su esposa Acacia (Carmen Viance), el hijo del matrimonio y el abuelo Martín (Víctor Pastor), invidente. Un enfrentamiento entre Juan y Lucas (Ramón Meca), el cacique y usurero local, provoca que el labrador sea encarcelado. Magdalena (Amelia Muñoz), convence a Acacia para que abandone una aldea empobrecida sobre la parece pesar una maldición. Tres años después, Juan encuentra a su esposa con otro hombre en el reservado de una taberna y la obliga a volver al hogar y mantener las apariencias hasta que fallezca el enfermo abuelo Martín. (Covertext)
    Content: Extras: Galería de fotos, Filmografías, Tres aldeas, Documentos de época, Florián Rey, Críticas, Entrevistas.
    Note: Ländercode: Multizona
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  • 10
    Online Resource
    Online Resource
    Boca Raton :CRC Press,
    UID:
    almahu_9949865920102882
    Format: 1 online resource , illustrations (colour)
    ISBN: 9781351128360 , 1351128361
    Content: Industrial Applications of Machine Learning 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. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka.
    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.
    Language: English
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