An improved online multiple kernel classification algorithm based on double updating online learning

2014 ◽  
Author(s):  
Yulin Xiao ◽  
Shangping Zhong
Keyword(s):  
Online Learning ◽  
Classification Algorithm ◽  
Multiple Kernel ◽  
Kernel Classification

scholarly journals Online Multiple Kernel Classification

2012 ◽  
Vol 90 (2) ◽  
pp. 289-316 ◽  
Author(s):  
Steven C. H. Hoi ◽  
Rong Jin ◽  
Peilin Zhao ◽  
Tianbao Yang
Keyword(s):  
Multiple Kernel ◽  
Kernel Classification

scholarly journals Learning Relevant Image Features With Multiple-Kernel Classification

2010 ◽  
Vol 48 (10) ◽  
pp. 3780-3791 ◽  
Author(s):  
Devis Tuia ◽  
Gustavo Camps-Valls ◽  
Giona Matasci ◽  
Mikhail Kanevski
Keyword(s):  
Image Features ◽  
Multiple Kernel ◽  
Kernel Classification

Efficient Multiple Kernel Classification Using Feature and Decision Level Fusion

2017 ◽  
Vol 25 (6) ◽  
pp. 1403-1416 ◽  
Author(s):  
Anthony J. Pinar ◽  
Joseph Rice ◽  
Lequn Hu ◽  
Derek T. Anderson ◽  
Timothy C. Havens
Keyword(s):  
Decision Level ◽  
Multiple Kernel ◽  
Decision Level Fusion ◽  
Kernel Classification ◽  
Level Fusion

A multiple kernel classification approach based on a Quadratic Successive Geometric Segmentation methodology with a fault diagnosis case

2018 ◽  
Vol 74 ◽  
pp. 209-216 ◽  
Author(s):  
Leonardo M. Honório ◽  
Daniele A. Barbosa ◽  
Edimar J. Oliveira ◽  
Paulo A. Nepomuceno Garcia ◽  
Murillo F. Santos
Keyword(s):  
Fault Diagnosis ◽  
Classification Approach ◽  
Multiple Kernel ◽  
Geometric Segmentation ◽  
Kernel Classification

A Novel Method for Palmprint Recognition Based on Tensor Subspace Learning

2010 ◽  
Vol 439-440 ◽  
pp. 1398-1403
Author(s):  
Yong Liang Xiao
Keyword(s):  
Local Binary Pattern ◽  
Subspace Learning ◽  
Palmprint Recognition ◽  
Svm Classifier ◽  
Palmprint Identification ◽  
Multiple Kernel ◽  
Novel Method ◽  
Tensor Subspace ◽  
Kernel Classification ◽  
Kernel Svm

Recently, palmprint identification has been developed for security purpose. In this paper, we propose a novel palmprint recognition scheme which has three features: 1) representation of palmprint images by Local Binary Pattern (LBP); 2) dimensionality reduction by tensor subspace learning; and 3) recognition by multiple kernel classification method based on tensor analysis. LBP can effectively capture substantial palm features while keeping robustness to illumination. Then we reduce the dimensionality of each palmprint samples based on tensor subspace learning which can preserve the spatial structure of LBP. Tensor multiple kernel SVM classifier is finally employed for palmprint recognition. Experimental results on PolyU palmprint database show the effectiveness of the proposed method.

Multiple Kernel Feature Fusion Using Kernel Fisher Method

2013 ◽  
Vol 333-335 ◽  
pp. 1406-1409
Author(s):  
Bo Yang
Keyword(s):  
Feature Fusion ◽  
Kernel Method ◽  
Kernel Space ◽  
Mapping Space ◽  
Weighted Fusion ◽  
Multiple Kernel ◽  
Fisher Method ◽  
Empirical Kernel Mapping ◽  
Kernel Classification ◽  
Kernel Mapping

Different from the existing multiple kernel methods which mainly work in implicit kernel space, we propose a novel multiple kernel method in empirical kernel mapping space. In empirical kernel mapping space, the combination of kernels can be treated as the weighted fusion of empirical kernel mapping samples. Based this fact, we developed a multiple kernel Fisher method to realize multiple kernel classification in empirical kernel mapping space. The experiments here illustrate that the proposed multiple kernel fisher method is feasible and effective.

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