Transductive multilabel learning via label set propagation

IEEE Transactions on Knowledge and Data Engineering

Volumn 25, Issue 3, 2013, Pages 704-719

  Kong, Xiangnan ^a   Ng, Michael K ^b   Zhou, Zhi Hua ^a  

^a NANJING UNIVERSITY   (China)

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

Data mining; machine learning; multilabel learning; semi supervised learning; transductive learning; unlabeled data

Indexed keywords

AUTOMATIC IMAGE ANNOTATION; CLOSED FORM SOLUTIONS; EFFECTIVE ALGORITHMS; EMPIRICAL STUDIES; GENE-FUNCTION ANALYSIS; LABEL SETS; LABELED AND UNLABELED DATA; LABELED TRAINING DATA; LEARNING METHODS; LEARNING TASKS; MULTI-LABEL; MULTIPLE CLASS; MULTIPLE LABELS; OPTIMIZATION PROBLEMS; REAL-WORLD APPLICATION; SEMI-SUPERVISED LEARNING; SET PROPAGATION; TRANSDUCTIVE LEARNING; UNLABELED DATA;

DATA MINING; LEARNING SYSTEMS; OPTIMIZATION; RAILROAD ROLLING STOCK; SUPERVISED LEARNING;

LEARNING ALGORITHMS;

EID: 84873289445     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2011.141     Document Type: Article

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