000 03622nam a22005295i 4500
001 978-3-031-01571-7
003 DE-He213
005 20240730163630.0
007 cr nn 008mamaa
008 220601s2014 sz | s |||| 0|eng d
020 _a9783031015717
_9978-3-031-01571-7
024 7 _a10.1007/978-3-031-01571-7
_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 _aSubramanya, Amarnag.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979629
245 1 0 _aGraph-Based Semi-Supervised Learning
_h[electronic resource] /
_cby Amarnag Subramanya, Partha Pratim Talukdar.
250 _a1st ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXIII, 111 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 _aIntroduction -- Graph Construction -- Learning and Inference -- Scalability -- Applications -- Future Work -- Bibliography -- Authors' Biographies -- Index .
520 _aWhile labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_979630
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 _aTalukdar, Partha Pratim.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979631
710 2 _aSpringerLink (Online service)
_979632
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004438
776 0 8 _iPrinted edition:
_z9783031026997
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_979633
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01571-7
912 _aZDB-2-SXSC
942 _cEBK
999 _c84816
_d84816