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1. |
Record Nr. |
UNINA9910705163103321 |
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Autore |
Leckenby Donavin A. |
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Titolo |
Wildlife habitats in managed rangelands : the Great Basin of southeastern Oregon : mule deer / / Donavin A. Leckenby [and eight others] |
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Pubbl/distr/stampa |
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Portland, Oregon : , : Pacific Northwest Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture, , 1982 |
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Descrizione fisica |
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1 online resource (40 pages) : illustrations |
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Collana |
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General technical report PNW ; ; 139 |
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Soggetti |
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Mule deer - Oregon |
Wildlife habitat improvement - Oregon |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from title screen (viewed Jan. 21, 2014). |
"June 1982." |
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Nota di bibliografia |
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Includes bibliographical references (pages 32-38). |
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2. |
Record Nr. |
UNINA9910702570303321 |
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Autore |
Masbruch Melissa D (Melissa Dawn) |
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Titolo |
Groundwater and surface-water resources in the Bureau of Land Management Moab master leasing plan area and adjacent areas, Grand and San Juan Counties, Utah, and Mesa and Montrose Counties, Colorado [[electronic resource] /] / by Melissa D. Masbruch and Christopher L Shope |
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Pubbl/distr/stampa |
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Reston, Va. : , : U.S. Dept. of the Interior, U.S. Geological Survey, , 2014 |
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Descrizione fisica |
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1 online resource (vi, 85 pages) : color illustrations, color maps |
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Collana |
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Open-file report ; ; 2014-1062 |
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Altri autori (Persone) |
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Soggetti |
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Groundwater - Quality - Utah |
Groundwater - Quality - Colorado |
Water-supply - Utah |
Water-supply - Colorado |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from title page (viewed on May 21, 2014). |
"Prepared in cooperation with the Bureau of Land Management." |
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Nota di bibliografia |
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Includes bibliographical references (pages 82-85). |
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3. |
Record Nr. |
UNINA9910831001503321 |
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Autore |
Bartz Eva |
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Titolo |
Online Machine Learning : A Practical Guide with Examples in Python / / edited by Eva Bartz, Thomas Bartz-Beielstein |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (163 pages) |
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Collana |
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Machine Learning: Foundations, Methodologies, and Applications, , 2730-9916 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Machine learning |
Artificial Intelligence |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Chapter 1:Introduction -- Chapter 2:Supervised Learning -- Chapter 3:Drift Detection and Handling -- Chapter 4:Initial Selection and Subsequent Updating of OML Models -- Chapter 5:Evaluation and Performance Measurement -- Chapter 6:Special Requirements for OML Methods -- Chapter 7:Practical Applications of Online Machine Learning -- Chapter 8:Open-Source-Software for Online Machine Learning -- Chapter 9:An Experimental Comparison of Batch and Online Machine Learning Algorithms -- Chapter 10:Hyperparameter Tuning -- Chapter 11:Summary and Outlook. |
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Sommario/riassunto |
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This book deals with the exciting, seminal topic of Online Machine Learning (OML). The content is divided into three parts: the first part looks in detail at the theoretical foundations of OML, comparing it to Batch Machine Learning (BML) and discussing what criteria should be developed for a meaningful comparison. The second part provides practical considerations, and the third part substantiates them with concrete practical applications. The book is equally suitable as a reference manual for experts dealing with OML, as a textbook for beginners who want to deal with OML, and as a scientific publication |
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for scientists dealing with OML since it reflects the latest state of research. But it can also serve as quasi OML consulting since decision-makers and practitioners can use the explanations to tailor OML to their needs and use it for their application and ask whether the benefits of OML might outweigh the costs. OML will soon become practical; it is worthwhile to get involved with it now. This book already presents some tools that will facilitate the practice of OML in the future. A promising breakthrough is expected because practice shows that due to the large amounts of data that accumulate, the previous BML is no longer sufficient. OML is the solution to evaluate and process data streams in real-time and deliver results that are relevant for practice. |
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