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ISBN 10: 0262045095 ISBN 13: 9780262045094
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Añadir al carritoTaschenbuch. Condición: Neu. Machine Learning Classification of Epileptic Seizures Based on EEG | using Third-Ordered Cumulants and Adaptive Fractal Analysis Techniques | Buchanna Gajula | Taschenbuch | Englisch | 2023 | LAP LAMBERT Academic Publishing | EAN 9786206152965 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Publicado por LAP LAMBERT Academic Publishing, 2023
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Añadir al carritoHardcover. Condición: Sehr gut. Gebraucht - Sehr gut Sg - leichte Beschädigungen oder Verschmutzungen, ungelesenes Mängelexemplar, gestempelt - A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions.Most tasks require a person or an automated system to reason to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
Idioma: Inglés
Publicado por Information Science Reference, 2010
ISBN 10: 161692859X ISBN 13: 9781616928599
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Publicado por LAP LAMBERT Academic Publishing, 2023
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning Classification of Epileptic Seizures Based on Electroencephalogram using Third-Ordered Cumulants and Adaptive Fractal Analysis Techniques1 IntroductionEpilepsy is one of the serious neurological diseases in the world. Indeed, early detection of epileptic seizures will extend the life span of epileptic patients. In this regard, a lot of efforts has been done to predict epileptic seizures based on electroencephalography (EEG) signals. In literature, there are many feature-based seizure classification methods quoted. No method is proved perfectly in capturing a standard set of features with the dynamics of signals. It is a common neurological disorder caused by the abnormally rapid release of brain nerve cells that is characterized by seizures. The scalp or intracranial Electroencephalogram (EEG) signals obtained in the clinic typically exhibit characteristics such as chaos, nonlinearity, etc.Deep neural networks have made significant advances in the field of machine learning during the last era. The model can learn effective representations from raw data in both supervised and unsupervised contexts by creating a hierarchical or 'deep' structure.
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Añadir al carritoHRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Idioma: Inglés
Publicado por Information Science Reference, 2010
ISBN 10: 161692859X ISBN 13: 9781616928599
Librería: moluna, Greven, Alemania
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Añadir al carritoGebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides current information on multimedia retrieval, advances the field of multimedia databases, and educates the multimedia database community. It is a critical text for professionals who are engaged in efforts to understand machine learning techniques fo.
Idioma: Inglés
Publicado por Information Science Reference, 2010
ISBN 10: 161692859X ISBN 13: 9781616928599
Librería: preigu, Osnabrück, Alemania
EUR 196,50
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Añadir al carritoBuch. Condición: Neu. Machine Learning Techniques for Adaptive Multimedia Retrieval | Technologies Applications and Perspectives | Chia-Hung Wei (u. a.) | Buch | Gebunden | Englisch | 2010 | Information Science Reference | EAN 9781616928599 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Añadir al carritoCondición: New. Print on Demand pp. 410.
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Añadir al carritoCondición: New. Print on Demand pp. 410 13:B&W 8.5 x 11 in or 280 x 216 mm Case Laminate on White w/Gloss Lam.
Idioma: Inglés
Publicado por Information Science Reference, 2010
ISBN 10: 161692859X ISBN 13: 9781616928599
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 236,75
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Añadir al carritoBuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As the size of multimedia databases grows, retrieval becomes a key challenge in multimedia database management. Accordingly, it is necessary to apply machine learning techniques to automatically tune the mechanism of multimedia retrieval systems. Machine Learning Techniques for Adaptive Multimedia Retrieval: Technologies Applications and Perspectives disseminates current information on multimedia retrieval, advances the field of multimedia databases, and educates the multimedia database community. It is a critical text for professionals who are engaged in efforts to understand machine learning techniques for adaptive multimedia retrieval research, design and applications.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 295,17
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Añadir al carritoCondición: New. PRINT ON DEMAND pp. 410.