.png){kind=icon}
Return
Support vector machine with eagle loss function
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2023.122168.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
SVM utilizes the hinge loss function and maximum margin to find the separating hyperplane. In SVM, only the boundary instances/support vectors confine the separating hyperplane, making it susceptible to noisy samples near the decision boundary. This work proposes a novel noise-robust eagle loss function and presents Eagle-SVM based on the proposed loss function. The formulation of the eagle loss function was motivated by examining the state-of-the-art loss assignment policy. It allocates the loss value as per instance significance. The more important instances are assigned higher loss values, whereas those corresponding to outliers and noise are assigned lower loss values. The experiments were conducted on the benchmark datasets downloaded from the UCI repository to compare the performance of the proposed variant of SVM with hinge loss SVM, pinball loss SVM, e-pinball loss SVM, SVM-CL, relabel SVM, 2medianSVM and 2meanSVM. The experimental results demonstrate that the eagle loss SVM outperforms all the state-of-the-art variants of SVM and is robust due to the incorporation of the novel loss assignment policy.
Keywords:
Novel loss function
Eagle loss SVM
Hinge loss SVM
Pinball loss SVM
Noise robust classifier
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveM Pathway and Areas 44 and 45 Are Involved in Stereoscopic Recognition Based on Binocular Disparity.
OAAI
OAAIShareSaveShareSaveShareSave
View more