ShareBoost: Boosting for multi-view learning with performance guarantees

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

Volumn 6912 LNAI, Issue PART 2, 2011, Pages 597-612

  Peng, Jing ^a,b,c,d,e,f   Barbu, Costin ^a,b,c,d,e,f   Seetharaman, Guna ^a,b,c,d,e,f   Fan, Wei ^a,b,c,d,e,f   Wu, Xian ^a,b,c,d,e,f   Palaniappan, Kannappan ^a,b,c,d,e,f  

^a MONTCLAIR STATE UNIVERSITY   (United States)

^b MIT LINCOLN LABORATORY   (United States)

^c AIR FORCE RESEARCH LABORATORY   (United States)

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

^e IBM CHINA RESEARCH LAB   (China)

^f UNIVERSITY OF MISSOURI   (United States)

Author keywords

boosting; convergence; Data fusion; multi view learning

Indexed keywords

BASE CLASSIFIERS; BOOSTING; CONVERGENCE; DATA TYPE; HIGH PROBABILITY; INFORMATION SOURCES; MULTI ARMED BANDIT; MULTI-VIEW LEARNING; MULTI-VIEWS; OBJECT OF INTERESTS; PARTIAL VIEWS; PERFORMANCE GUARANTEES; RESAMPLING; SAMPLING MECHANISMS; TRAINING ERRORS;

ALGORITHMS; DATA FUSION; FORECASTING; LEARNING SYSTEMS;

ADAPTIVE BOOSTING;

EID: 80052412839     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23783-6_38     Document Type: Conference Paper

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