Cross validation framework to choose amongst models and datasets for transfer learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6323 LNAI, Issue PART 3, 2010, Pages 547-562

  Zhong, Erheng ^a   Fan, Wei ^b   Yang, Qiang ^c   Verscheure, Olivier ^b   Ren, Jiangtao ^a  

^a SUN YAT SEN UNIVERSITY   (China)

^b IBM T J WATSON RESEARCH CENTER   (United States)

^c HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

CONDITIONAL DISTRIBUTION; CROSS VALIDATION; CROSS-DOMAIN; DATA SETS; DENSITY RATIO; DIFFERENT DISTRIBUTIONS; LEARNING PERFORMANCE; MARGINAL DISTRIBUTION; MODEL APPROXIMATES; OPEN PROBLEMS; REAL-WORLD APPLICATION; SHIFT-AND; STATE-OF-THE-ART METHODS; TARGET DOMAIN; TEXT CATEGORIZATION; TRANSFER LEARNING; USER RANKINGS; WINE QUALITY;

TEXT PROCESSING; WINE;

LEARNING SYSTEMS;

EID: 77958032545     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15939-8_35     Document Type: Conference Paper

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