Ensemble of Multiple Kernel SVM Classifiers

2014 ◽  
pp. 239-250 ◽  
Author(s):  
Xiaoguang Wang ◽  
Xuan Liu ◽  
Nathalie Japkowicz ◽  
Stan Matwin
Keyword(s):  
Multiple Kernel ◽  
Kernel Svm

scholarly journals Multiple Kernel SVM Based on Two-Stage Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 101133-101144
Author(s):  
Xingrui Gong ◽  
Bin Zou ◽  
Yuze Duan ◽  
Jie Xu ◽  
Qingxin Luo ◽  
...  
Keyword(s):  
Two Stage ◽  
Multiple Kernel ◽  
Kernel Svm

Multi-Objects Detection in Remote Sensing Images Using Multiple Kernel Learning

2012 ◽  
Vol 532-533 ◽  
pp. 1258-1262
Author(s):  
Xiang Juan Li ◽  
Hao Sun ◽  
Xin Wei Zheng ◽  
Xian Sun ◽  
Hong Qi Wang
Keyword(s):  
Remote Sensing ◽  
Multiple Kernel Learning ◽  
Sliding Window ◽  
Google Earth ◽  
Kernel Learning ◽  
Remote Sensing Images ◽  
Multiple Objects ◽  
Multiple Kernel ◽  
Objects Detection ◽  
Kernel Svm

The objective of this work is multiple objects detection in remote sensing images. Many classifiers have been proposed to detect military objects. In this paper, we demonstrate that linear combination of kernels can get a better classification precision than product of kernels. Starting with base kernels, we obtain different weights for each class through learning. Experiment on Caltech-101 dataset shows the learnt kernels yields superior classification results compared with single-kernel SVM. While such a powerful classifier act as a sliding-window detector to search planes in images collected from Google Earth, results shows the effectiveness of using MKL detector to locate military objects in remote sensing images.

Building Sparse Multiple-Kernel SVM Classifiers

2009 ◽  
Vol 20 (5) ◽  
pp. 827-839 ◽  
Author(s):  
Mingqing Hu ◽  
Yiqiang Chen ◽  
J.T.-Y. Kwok
Keyword(s):  
Multiple Kernel ◽  
Kernel Svm

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.

scholarly journals Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM

2015 ◽  
Vol 36 (6) ◽  
pp. 2118-2131 ◽  
Author(s):  
Martin Dyrba ◽  
Michel Grothe ◽  
Thomas Kirste ◽  
Stefan J. Teipel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Multimodal Analysis ◽  
Multiple Kernel ◽  
Kernel Svm

Nonlinear mixed pixel decomposition of hyperspectral imagery based on multiple kernel SVM

2014 ◽  
Vol 22 (7) ◽  
pp. 1912-1920
Author(s):  
谭熊 TAN Xiong ◽  
余旭初 YU Xu-chu ◽  
张鹏强 ZHANG Peng-qiang ◽  
秦进春 Qin Jin-chun
Keyword(s):  
Hyperspectral Imagery ◽  
Mixed Pixel ◽  
Multiple Kernel ◽  
Kernel Svm
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