Mining distinction and commonality across multiple domains using generative model for text classification

IEEE Transactions on Knowledge and Data Engineering

Volumn 24, Issue 11, 2012, Pages 2025-2039

  Zhuang, Fuzhen ^a   Luo, Ping ^b   Shen, Zhiyong ^b   He, Qing ^a   Xiong, Yuhong ^c   Shi, Zhongzhi ^a   Xiong, Hui ^d  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b HEWLETT PACKARD LABORATORIES   (United States)

^c Lashoucom   (China)

^d RUTGERS UNIVERSITY   (United States)

Author keywords

classification; cross domain learning; distinction and commonality; Statistical generative models

Indexed keywords

CLASSIFICATION ACCURACY; CLASSIFICATION TASKS; COLLABORATIVE TRAINING; CROSS-DOMAIN; DATA DISTRIBUTION; DIFFERENT DOMAINS; DISTINCTION AND COMMONALITY; EXPECTATION-MAXIMIZATION ALGORITHMS; GENERATIVE MODEL; INDEX WORD; KNOWLEDGE TRANSFORMATION; LATENT FACTOR; LATENT VARIABLE; LOCAL DATA; LOCAL REFINEMENT; MULTIPLE DOMAINS; MULTIPLE SOURCE; MULTIPLE TARGETS; PRIVACY CONCERNS; PROBABILISTIC LATENT SEMANTIC ANALYSIS; STATE-OF-THE-ART METHODS; STATISTICAL MODELS; TEXT CATEGORIZATION; TEXT CLASSIFICATION; TRAINING PHASE; TRANSFER LEARNING; TWO PHASE METHOD;

CLASSIFICATION (OF INFORMATION); IMAGE RETRIEVAL; TEXT PROCESSING;

ALGORITHMS;

EID: 84866920290     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2011.143     Document Type: Article

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