Paper Info
Title
Matrix-pattern-oriented classifier with boundary projection discrimination.
Abstract
The matrix-pattern-oriented Ho-Kashyap classifier (MatMHKS), utilizing two-sided weight vectors to constrain the matrix-based pattern, extends the representation of sample from vector to matrix. To further improve the classification ability of MatMHKS, we introduce a new regularization term into MatMHKS to form a new algorithm named BPDMatMHKS. In detail, we first divide the samples into three types including noise sample, fuzzy sample and boundary sample. Then, we combine the projection discrimination with these boundary samples, thus proposing the regularization term which concerns the priori structural information of the boundary samples. By doing so, the classification ability of MatMHKS has been further improved. Experiments validate the effectiveness and efficiency of the proposed BPDMatMHKS.
Year
DOI
Venue
2018
10.1016/j.knosys.2017.12.024
Knowledge-Based Systems
Keywords
Field
DocType
Matrix-based classifier,Boundary sample,Projection discrimination,Regularization learning,Pattern recognition
Data mining,Pattern recognition,Matrix pattern,Computer science,Matrix (mathematics),Fuzzy logic,Regularization (mathematics),Artificial intelligence,Classifier (linguistics)
Journal
Volume
ISSN
Citations 
149
0950-7051
0
PageRank 
References 
Authors
0.34
18
2
Name
Order
Citations
PageRank
Zhe Wang15020.04
Zonghai Zhu2113.54