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

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 Discrete Multiple Kernel k-means

2021 ◽  
Author(s):  
Rong Wang ◽  
Jitao Lu ◽  
Yihang Lu ◽  
Feiping Nie ◽  
Xuelong Li
Keyword(s):  
Optimization Algorithm ◽  
Information Loss ◽  
Complementary Information ◽  
Two Stage ◽  
Practical Applications ◽  
Multiple Kernel ◽  
Proposed Model ◽  
Kernel Fusion ◽  
Indicator Matrix ◽  
Two Stages

The multiple kernel k-means (MKKM) and its variants utilize complementary information from different kernels, achieving better performance than kernel k-means (KKM). However, the optimization procedures of previous works all comprise two stages, learning the continuous relaxed label matrix and obtaining the discrete one by extra discretization procedures. Such a two-stage strategy gives rise to a mismatched problem and severe information loss. To address this problem, we elaborate a novel Discrete Multiple Kernel k-means (DMKKM) model solved by an optimization algorithm that directly obtains the cluster indicator matrix without subsequent discretization procedures. Moreover, DMKKM can strictly measure the correlations among kernels, which is capable of enhancing kernel fusion by reducing redundancy and improving diversity. What’s more, DMKKM is parameter-free avoiding intractable hyperparameter tuning, which makes it feasible in practical applications. Extensive experiments illustrated the effectiveness and superiority of the proposed model.

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
Sign in / Sign up

Export Citation Format

Share Document