On minimum distribution discrepancy support vector machine for domain adaptation

DOI10.1016/j.patcog.2012.04.014zbMath1242.68252OpenAlexW1971816655MaRDI QIDQ437778

Jianwen Tao, Shi-Tong Wang, Fu-lai Chung

Publication date: 19 July 2012

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2012.04.014

zbMATH Keywords

pattern classificationsupport vector machinedomain adaptation learningmaximum mean discrepancymaximum scatter discrepancy

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items

L1-norm locally linear representation regularization multi-source adaptation learning, Robust latent regression with discriminative regularization by leveraging auxiliary knowledge, Multi-source adaptation joint kernel sparse representation for visual classification, Transfer affinity propagation-based clustering, Sub-domain adaptation learning methodology, A novel multi-task TSK fuzzy classifier and its enhanced version for labeling-risk-aware multi-task classification, Robust multi-source adaptation visual classification using supervised low-rank representation

Uses Software

• JGibbLDA

Cites Work

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