000 03409nam a22005175i 4500
001 978-3-031-01560-1
003 DE-He213
005 20240730163628.0
007 cr nn 008mamaa
008 220601s2012 sz | s |||| 0|eng d
020 _a9783031015601
_9978-3-031-01560-1
024 7 _a10.1007/978-3-031-01560-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aSettles, Burr.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979607
245 1 0 _aActive Learning
_h[electronic resource] /
_cby Burr Settles.
250 _a1st ed. 2012.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2012.
300 _aXIV, 100 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979608
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
710 2 _aSpringerLink (Online service)
_979609
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004322
776 0 8 _iPrinted edition:
_z9783031026881
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979610
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01560-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c84811
_d84811