The generalization error bound for the multiclass analytical center classifier.
This paper presents the multiclass classifier based on analytical center of feasible space (MACM). This multiclass classifier is formulated as quadratic constrained linear optimization and does not need repeatedly constructing classifiers to separate a single class from all the others. Its generalization error upper bound is proved theoretically. The experiments on benchmark datasets validate the generalization performance of MACM.
Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha 410082, China.
Zeng
Z
Fanzi
Key Laboratory for Embedded and Network Computing of Hunan Province, Hunan University, Changsha 410082, China.
Ma
M
Xiaolong
D000465:Algorithms; D001185:Artificial Intelligence / Q000706:statistics & numerical data*; D002965:Classification / Q000379:methods*; D016208:Databases, Factual / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D060388:Support Vector Machine