000 | 03409nam a22005175i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/978-3-031-01560-1 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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_aCOM004000 _2bisacsh |
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_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aSettles, Burr. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _979607 |
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245 | 1 | 0 |
_aActive Learning _h[electronic resource] / _cby Burr Settles. |
250 | _a1st ed. 2012. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2012. |
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300 |
_aXIV, 100 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSynthesis Lectures on Artificial Intelligence and Machine Learning, _x1939-4616 |
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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 |
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650 | 0 |
_aMachine learning. _91831 |
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650 | 0 |
_aNeural networks (Computer science) . _979608 |
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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 |
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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 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-01560-1 |
912 | _aZDB-2-SXSC | ||
942 | _cEBK | ||
999 |
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