.png){kind=icon}
.png){kind=icon}
Return
Local-CGFC: A Local Cumulant Generating Function Classification Rule
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2026.3652119.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
A classification rule based on the cumulant generating function of the training data, called the Cumulant Generating Function Classifier (CGFC), has been recently proposed, and has shown promising performance in terms of improved classification accuracy and robustness against noises. This paper first presents a new information-theoretical explanation of CGFC which indeed makes a classification by minimizing sample mutual information. The original CGFC is a type of global model, and a new variant, called Local-CGFC, is further introduced in this paper to achieve a local classification rule. Experimental studies on real-life datasets demonstrate the effectiveness of the proposed classifier and further illustrate its great potential for a number of real-world applications.
Keywords:
Classification
cumulant generating functions
mutual information minimization
local classification model
Journal
I
IF:
3.9
Papers:
610
Citations:
0