title: Kernel association for classification and prediction: a survey.
abstract: Kernel association (KA) in statistical pattern recognition used for classification and prediction have recently emerged in a machine learning and signal processing context. This survey outlines the latest trends and innovations of a kernel framework for big data analysis. KA topics include offline learning, distributed database, online learning, and its prediction. The structural presentation and the comprehensive list of references are geared to provide a useful overview of this evolving field for both specialists and relevant scholars.
authors.forename: Yuichi
authors.initials: Y
authors.lastname: Motai
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence / Q000145:classification* / Q000639:trends*; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000145:classification* / Q000639:trends*; D012815:Signal Processing, Computer-Assisted