.png){kind=icon}
Return
Matrix-pattern-oriented classifier with boundary projection discrimination
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2017.12.024.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Matrix-based classifier
Boundary sample
Projection discrimination
Regularization learning
Pattern recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSaveShareSave
View more