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008			230211s2012 sz | s |||| 0|eng d
020			_a9783031015601
024	7		_a10.1007/978-3-031-01560-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.75 _b2012 EB
100	1		_aSettles, Burr _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686728
245	1	0	_aActive Learning _cby Burr Settles
250			_a1st edition 2012
264		1	_aCham _bSpringer International Publishing _c2012
300			_a1 recurso en línea (XIV, 100 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aAutomating Inquiry -- Uncertainty Sampling -- Searching Through the Hypothesis Space -- Minimizing Expected Error and Variance -- Exploiting Structure in Data -- Theory -- Practical Considerations.
520			_aThe key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations.
988			_aSynthesis Collection of Technology_2012
650		7	_2embne _9166090 _aAprendizaje automático
776	0	8	_iPrinted edition: _z9783031004322
776	0	8	_iPrinted edition: _z9783031026881
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01560-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI