stat841F18/: Difference between revisions
Jump to navigation
Jump to search
(Created page with "== Presented by == Yan Yu Chen, Qisi Deng, Hengxin Li, Bochao Zhang == Introduction == == Previous Work == == Motivation == == Model Architecture == == ILSVRC 20...") |
|||
| Line 2: | Line 2: | ||
Yan Yu Chen, Qisi Deng, Hengxin Li, Bochao Zhang | Yan Yu Chen, Qisi Deng, Hengxin Li, Bochao Zhang | ||
== Introduction == | == Introduction == | ||
In the past two decades, due to their surprising classi- fication capability, support vector machine (SVM) [1] and its variants [2]–[4] have been extensively used in classification applications. | |||
Least square support vector machine (LS-SVM) and proximal sup- port vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification appli- cations directly, although variants of LS-SVM and PSVM have been proposed to handle such cases. | |||
== Previous Work == | == Previous Work == | ||
Revision as of 22:26, 8 November 2018
Presented by
Yan Yu Chen, Qisi Deng, Hengxin Li, Bochao Zhang
Introduction
In the past two decades, due to their surprising classi- fication capability, support vector machine (SVM) [1] and its variants [2]–[4] have been extensively used in classification applications. Least square support vector machine (LS-SVM) and proximal sup- port vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM and PSVM cannot be used in regression and multiclass classification appli- cations directly, although variants of LS-SVM and PSVM have been proposed to handle such cases.