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336			_aTexto _btxt _2rdacontent
710	2		_aSpringerLink (Online service) _0http://id.loc.gov/authorities/names/no2005046756 _1http://viaf.org/viaf/148105729 _9106996
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008			181105s2018 gw | s |||| 0|eng d
020			_a9783319969787
024	7		_a10.1007/978-3-319-96978-7 _2doi
050		4	_aQH540
245	0	0	_aMachine Learning for Ecology and Sustainable Natural Resource Management _cedited by Grant Humphries, Dawn R. Magness, Falk Huettmann.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XXIV, 441 páginas 126 ilustraciones, 80 ilustraciones a color)
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
490	0		_aBiomedical and Life Sciences (Springer-11642)
505	0		_a1: 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.
520	3		_aEcologists 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, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often "messy" and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.
988			_aEBSPRINGER_BIOMEDLIFE_2019
650		7	_aEcología _2embne _9404984
700	1		_aHumphries, Grant _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _1http://viaf.org/viaf/136456019
700	1		_aMagness, Dawn R _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aHuettmann, Falk _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _1http://viaf.org/viaf/121588185 _993468
776	0	8	_iPrinted edition: _z9783319969763
776	0	8	_iPrinted edition: _z9783319969770
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-96978-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_aSI _aSI _aSI _b06/2019 _cm _dz _feng _ggw _h0 _eIG _zSI