A novel geometric approach to binary classification based on scaled convex hulls.
Geometric methods are very intuitive and provide a theoretical foundation to many optimization problems in the fields of pattern recognition and machine learning. In this brief, the notion of scaled convex hull (SCH) is defined and a set of theoretical results are exploited to support it. These results allow the existing nearest point algorithms to be directly applied to solve both the separable and nonseparable classification problems successfully and efficiently. Then, the popular S-K algorithm has been presented to solve the nonseparable problems in the context of the SCH framework. The theoretical analysis and some experiments show that the proposed method may achieve better performance than the state-of-the-art methods in terms of the number of kernel evaluations and the execution time.
Liu
Zhenbing
Z
State Key Lab for Multispectral Information Processing Technologies, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Hubei 430074, China. liuzb0618@hotmail.com
Liu
J G
JG
Pan
Chao
C
Wang
Guoyou
G
D000465:Algorithms; D001185:Artificial Intelligence; D008432:Mathematical Computing; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012984:Software; D012986:Software Validation