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Constructing support vector machines with missing data
Author(s) -
Stewart Thomas G.,
Zeng Donglin,
Wu Michael C.
Publication year - 2018
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1430
Subject(s) - missing data , support vector machine , computer science , artificial intelligence , machine learning , data set , data mining , classifier (uml) , structured support vector machine , set (abstract data type) , pattern recognition (psychology) , programming language
A proposed framework for constructing SVMs when the training set includes observations with complete predictor information (filled circles) and observations with incomplete predictor information (empty circles).