Sprache: Englisch
Verlag: Shaanxi peoples's fine arts publishing house January 1996, 1996
ISBN 10: 7536806655 ISBN 13: 9787536806658
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Soft Cover. Zustand: Very Good.
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Sprache: Englisch
Verlag: Indiana University Press 2013-01-25, 2013
ISBN 10: 0253006821 ISBN 13: 9780253006820
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In den WarenkorbHardcover. Zustand: New.
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Sprache: Chinesisch
Verlag: Electronic Industry Publishing House, 2009
ISBN 10: 7121085119 ISBN 13: 9787121085116
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In den WarenkorbPAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
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Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2026
ISBN 10: 1041010311 ISBN 13: 9781041010319
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In den WarenkorbPaperback. Zustand: New. This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.
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In den WarenkorbZustand: New.
Sprache: Englisch
Verlag: Taylor and Francis Ltd, GB, 2026
ISBN 10: 1041010311 ISBN 13: 9781041010319
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Paperback. Zustand: New. This book provides a thorough exploration of the latest innovations in AI for general time series analysis, distribution shift, and foundation models. It offers an in-depth look at cutting-edge techniques and methodologies, using advanced algorithms that are transforming time series analysis across industries. The authors highlight the use of AI models, particularly those based on deep learning, to study the sequence of data points collected at successive points in time.In the study of the use of AI for general time series analysis, readers are introduced to a recent important model like TimesNet, which has set new benchmarks for general time series analysis. TimesNet is a cutting-edge model for time series analysis, which transforms one-dimensional time series data into two-dimensional space to better capture temporal variations. This approach allows TimesNet to excel in various tasks such as short- and long-term forecasting, imputation, classification, and anomaly detection. The authors also discuss distribution shift in time series, with an important coverage on the use of AdaTime. This is a benchmarking suite for domain adaptation which addresses distribution shifts in time series data through Unsupervised Domain Adaptation (UDA). In the last section, a significant focus is placed on the emergence of time series foundation models, particularly for forecasting. The book explores pioneering models like Time-LLM, which are designed to offer universal forecasting capabilities across diverse time series tasks.The book can be used as supplementary reading for graduate students taking advanced topics/seminars on advanced deep learning and foundation models. It is also a useful reference for researchers and engineers working on time-series applications in finance, healthcare, energy, and climate.
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In den WarenkorbPaperback / softback. Zustand: New. New copy - Usually dispatched within 4 working days.
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In den WarenkorbPaperback. Zustand: Brand New. 246 pages. 9.18x6.12x9.21 inches. In Stock.
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In den WarenkorbHRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Sprache: Englisch
Verlag: Wiley-IEEE Computer Society Pr, 2013
ISBN 10: 1118345789 ISBN 13: 9781118345788
Anbieter: GreatBookPrices, Columbia, MD, USA
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Sprache: Englisch
Verlag: John Wiley & Sons Inc, New York, 2014
ISBN 10: 1118345789 ISBN 13: 9781118345788
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Erstausgabe
Hardcover. Zustand: new. Hardcover. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics This book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems. Featuring a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics: Highlights protein analysis applications such as protein-related drug activity comparisonIncorporates salient case studies illustrating how to apply the methods outlined in the bookTackles the complex relationship between proteins from a systems biology point of viewRelates the topic to other emerging technologies such as data mining and visualizationIncludes many tables and illustrations demonstrating concepts and performance figures Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics. An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics This book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.