Open AccessQuantitative convergence analysis of kernel based large-margin unified machines
Author(s) -
Jun Fan,
Dao Hong Xiang
Publication year - 2020
Publication title -
communications on pure and applied analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.077
H-Index - 42
eISSN - 1553-5258
pISSN - 1534-0392
DOI - 10.3934/cpaa.2020180
Subject(s) - reproducing kernel hilbert space , support vector machine , hilbert space , margin (machine learning) , computer science , binary classification , kernel method , convergence (economics) , dimension (graph theory) , kernel (algebra) , a priori and a posteriori , binary number , classifier (uml) , artificial intelligence , pattern recognition (psychology) , margin classifier , projection (relational algebra) , algorithm , machine learning , mathematics , discrete mathematics , mathematical analysis , philosophy , arithmetic , epistemology , pure mathematics , economic growth , economics