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A Sampling Method Based on Gauss Kernel Learning and the Expanding Research

Shunzhi Zhu, Kaibiao Lin, Zhiqiang Zeng, Lizhao Liu, Wenxing Hong
2012 Journal of Computers  
The method first preprocesses the data by oversampling the minority class in kernel space, and then the pre-images of the synthetic samples are found based on a distance relation between kernel space and  ...  scale invariant non-equilibrium .The paper presents a sample approach based on kernel learning to solve classification on imbalance dataset by Support Vector Machine (SVM).  ...  ACKNOWLEDGMENT This work is supported by the Natural Science Foundation of Fujian Province(2010J01353 ) ; Open Fund (201001) Funding from key lab of Spatial Data Mining and Information Sharing, Ministry  ... 
doi:10.4304/jcp.7.2.547-554 fatcat:fb47fbcq6bfxvj74njywzawpiy

A Robust Fuzzy Kernel Clustering Algorithm

Zhang Chen, Xia Shixiong, Liu Bing
2013 Applied Mathematics & Information Sciences  
Traditional fuzzy kernel clustering methods does Iterative clustering in the original data space or in the feature space by mapping the samples into high-dimensional feature space through a kernel function  ...  Meawhile, a kernel function parameter optimization method under the unsupervised condition is also proposed in this paper.  ...  Literature [15] presented a possibilistic fuzzy clustering algorithm based on the kernel function, which used the Gauss kernel function to design the distance based on the PFCM, overcoming the shortcomings  ... 
doi:10.12785/amis/070319 fatcat:reoc2is43rdnrcci4kvnbabzne

Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model

Yong Ma, Hao Liu, Guangyu Zhai, Zongjie Huo, Zhihan Lv
2021 Wireless Communications and Mobile Computing  
The support vector machine is a machine learning method based on the VC dimension theory of statistical learning and the principle of structural risk minimization.  ...  Taking 81 small- and medium-sized listed companies as the research object, this paper chooses the small- and medium-sized listed companies in every quarter of 2018 as the research sample.  ...  Then, a multiclass classification method based on the hybrid kernel function fuzzy support vector machine is proposed.  ... 
doi:10.1155/2021/7819011 fatcat:usochzmdsnbcln72z6t644niea

Evaluation and Exploitation of Retrieval Algorithms for Estimating Biophysical Crop Variables Using Sentinel-2, Venus, and PRISMA Satellite Data

Raffaele CASA,Deepak UPRETI,Angelo PALOMBO,Simone PASCUCCI,Hao YANG,Guijun YANG,Wenjiang HUANG,Stefano PIGNATTI
2020 Journal of Geodesy and Geoinformation Science  
Two different methodologies devoted to the estimation of biophysical crop variables Leaf area index (LAI) and Leaf chlorophyll content (Cab) were evaluated: non-kernel-based and kernel-based Machine Learning  ...  Whereas, for PRISMA simulated data the Kernel Ridge Regression (KRR) was the best performing algorithm among all the other MLRA (R2=0.91 and RMSE=0.51) for LAI and (R2=0.83 and RMSE=6.09) for Cab, respectively  ...  To make the training feasible with the kernelbased algorithms active learning techniques have been proposed that optimally sample the simulations generated by RTM in manner that the training of kernel  ... 
doi:10.11947/j.jggs.2020.0408 doaj:b5d502c0199c48fb8542a609a060ff99 fatcat:z6xbztv35zcyfgjlpmjeh4vwom

Learning to Diversify via Weighted Kernels for Classifier Ensemble [article]

Xu-Cheng Yin and Chun Yang and Hong-Wei Hao
2014 arXiv   pre-print
Given a measure formulation, the diversity is calculated with weighted kernels (i.e., the diversity is measured on the component classifiers' outputs which are kernelled and weighted), and the kernel weights  ...  In this paper, we argue that diversity, not direct diversity on samples but adaptive diversity with data, is highly correlated to ensemble accuracy, and we propose a novel technology for classifier ensemble  ...  In this paper, by introducing kernel methods, we expand existing diversity measures with weighted kernels, and propose a new diversity-based ensemble method, Learning TO Diversify via Weighted Kernels,  ... 
arXiv:1406.1167v1 fatcat:tvaqkcoefbc2dajcwfs5rxobt4

Research on power equipment recognition method based on image processing

Haiyan Wang, Fanwei Meng
2019 EURASIP Journal on Image and Video Processing  
And the method of power identification based on image processing proposed in this paper has good recognition accuracy.  ...  In order to realize automatic identification of power equipment, this paper presents a method of recognition of power equipment based on image processing.  ...  Acknowledgements The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions. About the authors Funding Not applicable.  ... 
doi:10.1186/s13640-019-0452-5 fatcat:672hoe7f7vbpxatvnmzrhq2qbi

Learning to Optimize Non-Rigid Tracking [article]

Yang Li, Aljaž Božič, Tianwei Zhang, Yanli Ji, Tatsuya Harada, Matthias Nießner
2020 arXiv   pre-print
One of the widespread solutions for non-rigid tracking has a nested-loop structure: with Gauss-Newton to minimize a tracking objective in the outer loop, and Preconditioned Conjugate Gradient (PCG) to  ...  Second, we bridge the gap between the preconditioning technique and learning method by introducing a ConditionNet which is trained to generate a preconditioner such that PCG can converge within a small  ...  Acknowledgements This work was also supported by a TUM-IAS Rudolf Mößbauer Fellowship, the ERC Starting Grant Scan2CAD  ... 
arXiv:2003.12230v1 fatcat:n4r25rvhzrc2fahx4fve54pole

A Review on Image Denoising using Bilateral Filtering

Shaweta Goyal
2018 International Journal for Research in Applied Science and Engineering Technology  
The bilateral filter is a non-linear technique that can denoise an image while respecting strong edges.  ...  This content gives a graphical, natural introduction to bilateral filtering, a practical guide for efficient implementation and an overview of its various applications, and in addition mathematical analysis  ...  Bilateral filtering method is based on the Gauss filtering method proposed in dealing with each adjacent pixel gray values.  ... 
doi:10.22214/ijraset.2018.3275 fatcat:peruq22o45atvjnxbuqot65qke

Disentangling the Gauss-Newton Method and Approximate Inference for Neural Networks [article]

Alexander Immer
2020 arXiv   pre-print
Algorithms that combine the Gauss-Newton method with the Laplace and Gaussian variational approximation have recently led to state-of-the-art results in Bayesian deep learning.  ...  In this thesis, we disentangle the generalized Gauss-Newton and approximate inference for Bayesian deep learning.  ...  deep learning and enabled new applications based on uncertainty [19, 38, 44, 55] .  ... 
arXiv:2007.11994v1 fatcat:do35k6mz6resnhnersq5vd4dim

Mars Image Super-Resolution Based on Generative Adversarial Network

Cong Wang, Yin Zang, Yongqiang Zhang, Rui Tian, Mingli Ding
2021 IEEE Access  
Nowadays, the mainstream image super-resolution methods are based on deep learning or CNNs, which are better than traditional methods.  ...  However, these deep learning based methods obtain low-resolution(LR) images usually by using an ideal down-sampling method (e.g. bicubic interpolation).  ...  Figure 1 shows the results that directly use the deep learning based methods on 'non-ideal' LR images.  ... 
doi:10.1109/access.2021.3101858 fatcat:2mikt3gehfhwfecd7w6lg2ty5i

Real-Time Construction Simulation Coupling a Concrete Temperature Field Interval Prediction Model with Optimized Hybrid-Kernel RVM for Arch Dams

Wenshuai Song, Tao Guan, Bingyu Ren, Jia Yu, Jiajun Wang, Binping Wu
2020 Energies  
Secondly, this paper proposes a concrete temperature interval prediction method based on the hybrid-kernel relevance vector machine (HK-RVM) with the improved grasshopper optimization algorithm (IGOA).  ...  The hybrid-kernel method is adopted to ensure the prediction accuracy and generalization ability of the model.  ...  The gauss kernel function is a type of local kernel function with a good local learning ability [42] . The concrete temperature data are dynamic and nonlinear.  ... 
doi:10.3390/en13174487 fatcat:kty7lw4zh5bxfikynzo2zqew7i

Learning to Optimize Non-Rigid Tracking

Yang Li, Aljaz Bozic, Tianwei Zhang, Yanli Ji, Tatsuya Harada, Matthias NieBner
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
One of the widespread solutions for non-rigid tracking has a nested-loop structure: with Gauss-Newton to minimize a tracking objective in the outer loop, and Preconditioned Conjugate Gradient (PCG) to  ...  Second, we bridge the gap between the preconditioning technique and learning method by introducing a ConditionNet which is trained to generate a preconditioner such that PCG can converge within a small  ...  Acknowledgements This work was also supported by a TUM-IAS Rudolf Mößbauer Fellowship, the ERC Starting Grant Scan2CAD (804724), and the German Research Foundation (DFG) Grant Making Machine Learning on  ... 
doi:10.1109/cvpr42600.2020.00496 dblp:conf/cvpr/LiBZJHN20 fatcat:7e7qvr3xxbgsxdhz2bpdng33lm

A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment

Yan Wang, Liguo Zhang
2017 Mathematical Problems in Engineering  
The fault diagnosis method based on dissolved gas analysis (DGA) is of great significance to detect the potential faults of the transformer and improve the security of the power system.  ...  Then, the training and deployment strategy of the combined diagnosis model is designed based on Storm and Spark platform, which provides a solution for the transformer fault diagnosis in big data environment  ...  Acknowledgments This work was supported by the National Natural Science Fund (51677072) and the Fundamental Research Funds for the Central Universities (2014MS132).  ... 
doi:10.1155/2017/9670290 fatcat:tgxsrmw4f5bwxnjaw2rrzzsama

Variable Selection for Nonparametric Learning with Power Series Kernels [article]

Kota Matsui, Wataru Kumagai, Kenta Kanamori, Mitsuaki Nishikimi, Takafumi Kanamori
2018 arXiv   pre-print
In this paper, we propose a variable selection method for general nonparametric kernel-based estimation.  ...  We see that the proposed method can be applied to various kernel nonparametric estimation such as kernel ridge regression, kernel-based density and density-ratio estimation.  ...  Acknowledgements This research was supported by JST-CREST (JPMJCR1412) from the Ministry of Education, Culture, Sports, Science and Technology of Japan.  ... 
arXiv:1806.00569v2 fatcat:fphskgbr5bgh5dcuh2jplahari

An Output Power Prediction Method for Multiple Wind Farms under Energy Internet Environment

Jianlou Lou, Hui Cao, Bin Song, Jizhe Xiao
2016 International Journal of Grid and Distributed Computing  
Secondly, delete outliers of different farms based on DBSCAN algorithm and select multiple wind fields training samples.  ...  And searching the optimal input parameters of LSSVM based on particle swarm algorithm to construct every wind farm model.  ...  Acknowledgments This paper is a revised and expanded version of a paper entitled Multi-wind Field Output Power Prediction Method based on Energy Internet and DBPSO-LSSVM presented at Information Science  ... 
doi:10.14257/ijgdc.2016.9.11.23 fatcat:mvug46fsnzhajprsdv4ul6pt3u
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