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001 978-3-031-79178-9
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020 _a9783031791789
_9978-3-031-79178-9
024 7 _a10.1007/978-3-031-79178-9
_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 _aJaskie, Kristen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983874
245 1 0 _aPositive Unlabeled Learning
_h[electronic resource] /
_cby Kristen Jaskie, Andreas Spanias.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXVII, 134 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 _aPreface -- Acknowledgments -- Introduction -- Problem Definition -- Evaluating the Positive Unlabeled Learning Problem -- Solving the PU Learning Problem -- Applications -- Summary -- Bibliography -- Authors' Biographies.
520 _aMachine learning and artificial intelligence (AI) are powerful tools that create predictive models, extract information, and help make complex decisions. They do this by examining an enormous quantity of labeled training data to find patterns too complex for human observation. However, in many real-world applications, well-labeled data can be difficult, expensive, or even impossible to obtain. In some cases, such as when identifying rare objects like new archeological sites or secret enemy military facilities in satellite images, acquiring labels could require months of trained human observers at incredible expense. Other times, as when attempting to predict disease infection during a pandemic such as COVID-19, reliable true labels may be nearly impossible to obtain early on due to lack of testing equipment or other factors. In that scenario, identifying even a small amount of truly negative data may be impossible due to the high false negative rate of available tests. In such problems, it is possible to label a small subset of data as belonging to the class of interest though it is impractical to manually label all data not of interest. We are left with a small set of positive labeled data and a large set of unknown and unlabeled data. Readers will explore this Positive and Unlabeled learning (PU learning) problem in depth. The book rigorously defines the PU learning problem, discusses several common assumptions that are frequently made about the problem and their implications, and considers how to evaluate solutions for this problem before describing several of the most popular algorithms to solve this problem. It explores several uses for PU learning including applications in biological/medical, business, security, and signal processing. This book also provides high-level summaries of several related learning problems such as one-class classification, anomaly detection, and noisy learning and their relation to PU learning.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_983876
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aSpanias, Andreas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983880
710 2 _aSpringerLink (Online service)
_983882
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031791833
776 0 8 _iPrinted edition:
_z9783031791734
776 0 8 _iPrinted edition:
_z9783031791888
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_983884
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79178-9
912 _aZDB-2-SXSC
942 _cEBK
999 _c85575
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