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A Sampling Method Based on Gauss Kernel Learning and the Expanding Research
2012
Journal of Computers
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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
2013
Applied Mathematics & Information Sciences
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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
2021
Wireless Communications and Mobile Computing
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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
2020
Journal of Geodesy and Geoinformation Science
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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 ｋｅｒｎｅｌ￣ｂａｓｅｄ ａｌｇｏｒｉｔｈｍｓ ａｃｔｉｖｅ ｌｅａｒｎｉｎｇ ｔｅｃｈｎｉｑｕｅｓ ｈａｖｅ ｂｅｅｎ ｐｒｏｐｏｓｅｄ ｔｈａｔ ｏｐｔｉｍａｌｌｙ ｓａｍｐｌｅ ｔｈｅ ｓｉｍｕｌａｔｉｏｎｓ ｇｅｎｅｒａｔｅｄ ｂｙ ＲＴＭ ｉｎ ａ 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]
2014
arXiv
pre-print
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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
2019
EURASIP Journal on Image and Video Processing
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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]
2020
arXiv
pre-print
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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
2018
International Journal for Research in Applied Science and Engineering Technology
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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]
2020
arXiv
pre-print
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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
2021
IEEE Access
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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
2020
Energies
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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
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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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
2017
Mathematical Problems in Engineering
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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]
2018
arXiv
pre-print
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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
2016
International Journal of Grid and Distributed Computing
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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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