000			02795nam a22004095i 4500
999			_c387893 _d387893
001			387893
003			ES-MaUEC
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007			cr nn 008mamaa
008			230429s2019 sz | s |||| 0|eng d
020			_a9783031023187
024	7		_a10.1007/978-3-031-02318-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.H84 _b2019 EB
100	1		_aAlonso, Omar _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688322
245	1	4	_aThe Practice of Crowdsourcing _cby Omar Alonso
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XIX, 129 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Information Concepts Retrieval and Services _x1947-9468
505	0		_aPreface -- Acknowledgments -- Introduction -- Designing and Developing Microtasks -- Quality Assurance -- Algorithms and Techniques for Quality Control -- The Human Side of Human Computation -- Putting All Things Together -- Systems and Data Pipelines -- Looking Ahead -- Bibliography -- Author's Biography .
520			_aMany data-intensive applications that use machine learning or artificial intelligence techniques depend on humans providing the initial dataset, enabling algorithms to process the rest or for other humans to evaluate the performance of such algorithms. Not only can labeled data for training and evaluation be collected faster, cheaper, and easier than ever before, but we now see the emergence of hybrid human-machine software that combines computations performed by humans and machines in conjunction. There are, however, real-world practical issues with the adoption of human computation and crowdsourcing. Building systems and data processing pipelines that require crowd computing remains difficult. In this book, we present practical considerations for designing and implementing tasks that require the use of humans and machines in combination with the goal of producing high-quality labels.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9667195 _aComputación evolutiva
776	0	8	_iPrinted edition: _z9783031002250
776	0	8	_iPrinted edition: _z9783031011900
776	0	8	_iPrinted edition: _z9783031034466
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02318-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI