Machine Learning for Ecology and Sustainable Natural Resource Management

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Humphries, Grant (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Magness, Dawn R. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Huettmann, Falk (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Θέματα: Ecology .; Bioinformatics .; Computational biology .; Biostatistics.; Data mining.; Pattern recognition.; Ecology.; Computer Appl. in Life Sciences.; Data Mining and Knowledge Discovery.; Pattern Recognition.

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology,...

Πίνακας περιεχομένων:

• 1: Introduction to Machine Learning A. Data-intensive science B. Data Issues and Availability
• 2: Data-mining in Ecological and Wildlife Research A. Multiple Methods in the Scientific Process B. Data-mining in Ecological and Wildlife Research C. Applications in Ecological Research a. Predicting Patterns in Space and Time b. Data Exploration and Hypothesis Generation c. Pattern Recognition for Sampling D. Bringing It All Together: Leveraging Multiple Methods to Increase Knowledge for Resource Management
• 3: Machine Learning and Resource Management A. Web-based Machine Learning Applications for Wildlife Management B. Linking Machine Learning in Management Applications C. Machine Learning and the Cloud for Natural Resource Applications D. The Global View: Hopes and Disappointments E. The Future of Machine Learning.