Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New.
Lingua: Inglese
Editore: Springer Verlag, Singapore, 2022
ISBN 10: 9811675651 ISBN 13: 9789811675652
Da: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: Springer Verlag, Singapore, 2022
ISBN 10: 9811675651 ISBN 13: 9789811675652
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
EUR 120,57
Quantità: 3 disponibili
Aggiungi al carrelloHRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 120,56
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Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition.
Da: Chiron Media, Wallingford, Regno Unito
EUR 130,18
Quantità: 3 disponibili
Aggiungi al carrelloHardcover. Condizione: New.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 129,82
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New. In.
Lingua: Inglese
Editore: Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 9811675651 ISBN 13: 9789811675652
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condizione: new. Hardcover. The development of information technology has made it possible to collect large amounts of spatial data on a daily basis. It is of enormous significance when it comes to discovering implicit, non-trivial and potentially valuable information from this spatial data. Spatial co-location patterns reveal the distribution rules of spatial features, which can be valuable for application users. This book provides commercial software developers with proven and effective algorithms for detecting and filtering these implicit patterns, and includes easily implemented pseudocode for all the algorithms. Furthermore, it offers a basis for further research in this promising field.Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors recent research, the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns.Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 146,36
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Da: Majestic Books, Hounslow, Regno Unito
EUR 142,64
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Springer Verlag, Singapore, Singapore, 2022
ISBN 10: 981163419X ISBN 13: 9789811634192
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condizione: new. Hardcover. This book presents the state of the art in distributed machine learning algorithms that are based on gradient optimization methods. In the big data era, large-scale datasets pose enormous challenges for the existing machine learning systems. As such, implementing machine learning algorithms in a distributed environment has become a key technology, and recent research has shown gradient-based iterative optimization to be an effective solution. Focusing on methods that can speed up large-scale gradient optimization through both algorithm optimizations and careful system implementations, the book introduces three essential techniques in designing a gradient optimization algorithm to train a distributed machine learning model: parallel strategy, data compression and synchronization protocol.Written in a tutorial style, it covers a range of topics, from fundamental knowledge to a number of carefully designed algorithms and systems of distributed machine learning. It will appealto a broad audience in the field of machine learning, artificial intelligence, big data and database management. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 151,95
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Springer Verlag, Singapore, SG, 2022
ISBN 10: 9811675651 ISBN 13: 9789811675652
Da: Rarewaves USA, OSWEGO, IL, U.S.A.
Hardback. Condizione: New. 2022 ed. The development of information technology has made it possible to collect large amounts of spatial data on a daily basis. It is of enormous significance when it comes to discovering implicit, non-trivial and potentially valuable information from this spatial data. Spatial co-location patterns reveal the distribution rules of spatial features, which can be valuable for application users. This book provides commercial software developers with proven and effective algorithms for detecting and filtering these implicit patterns, and includes easily implemented pseudocode for all the algorithms. Furthermore, it offers a basis for further research in this promising field.Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors' recent research, the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns.Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 137,60
Quantità: 1 disponibili
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 154,61
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Da: Speedyhen, Hertfordshire, Regno Unito
EUR 107,77
Quantità: 3 disponibili
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 156,11
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 154,95
Quantità: Più di 20 disponibili
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 154,95
Quantità: Più di 20 disponibili
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 147,78
Quantità: 3 disponibili
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 147,23
Quantità: Più di 20 disponibili
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 147,23
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 147,23
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 147,61
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 147,59
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 147,22
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 147,22
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 165,29
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 165,77
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Springer Verlag, Singapore, SG, 2022
ISBN 10: 9811675651 ISBN 13: 9789811675652
Da: Rarewaves.com USA, London, LONDO, Regno Unito
EUR 170,03
Quantità: 2 disponibili
Aggiungi al carrelloHardback. Condizione: New. 2022 ed. The development of information technology has made it possible to collect large amounts of spatial data on a daily basis. It is of enormous significance when it comes to discovering implicit, non-trivial and potentially valuable information from this spatial data. Spatial co-location patterns reveal the distribution rules of spatial features, which can be valuable for application users. This book provides commercial software developers with proven and effective algorithms for detecting and filtering these implicit patterns, and includes easily implemented pseudocode for all the algorithms. Furthermore, it offers a basis for further research in this promising field.Preference-based co-location pattern mining refers to mining constrained or condensed co-location patterns instead of mining all prevalent co-location patterns. Based on the authors' recent research, the book highlights techniques for solving a range of problems in this context, including maximal co-location pattern mining, closed co-location pattern mining, top-k co-location pattern mining, non-redundant co-location pattern mining, dominant co-location pattern mining, high utility co-location pattern mining, user-preferred co-location pattern mining, and similarity measures between spatial co-location patterns.Presenting a systematic, mathematical study of preference-based spatial co-location pattern mining, this book can be used both as a textbook for those new to the topic and as a reference resource for experienced professionals.