Kriging with Composite Kernel Learning for Surrogate Modeling in Computer Experiments

2019 ◽  
Author(s):  
Pramudita S. Palar ◽  
Koji Shimoyama
Keyword(s):  
Surrogate Modeling ◽  
Computer Experiments ◽  
Kernel Learning ◽  
Composite Kernel

Surrogate Modeling of Computer Experiments With Different Mesh Densities

Technometrics ◽  
2014 ◽  
Vol 56 (3) ◽  
pp. 372-380 ◽  
Author(s):  
Rui Tuo ◽  
C. F. Jeff Wu ◽  
Dan Yu
Keyword(s):  
Surrogate Modeling ◽  
Computer Experiments

Composite kernel learning network for hyperspectral image classification

2021 ◽  
Vol 42 (16) ◽  
pp. 6068-6091
Author(s):  
Zhe Wu ◽  
Jianjun Liu ◽  
Jinlong Yang ◽  
Zhiyong Xiao ◽  
Liang Xiao
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Kernel Learning ◽  
Hyperspectral Image Classification ◽  
Learning Network ◽  
Composite Kernel

scholarly journals EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP MAPPING USING SATELLITE IMAGE TIME-SERIES DATA

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 201-207
Author(s):  
S. Niazmardi ◽  
A. Safari ◽  
S. Homayouni
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Computational Time ◽  
Classification Algorithms ◽  
Multiple Kernel ◽  
Composite Kernel ◽  
Crop Mapping

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

Gaussian Process Surrogate Model with Composite Kernel Learning for Engineering Design

AIAA Journal ◽  
2020 ◽  
Vol 58 (4) ◽  
pp. 1864-1880 ◽  
Author(s):  
Pramudita Satria Palar ◽  
Lavi Rizki Zuhal ◽  
Koji Shimoyama
Keyword(s):  
Gaussian Process ◽  
Engineering Design ◽  
Surrogate Model ◽  
Kernel Learning ◽  
Composite Kernel

Making the Most Out of Surrogate Models: Tricks of the Trade

2010 ◽  
Author(s):  
Felipe A. C. Viana ◽  
Christian Gogu ◽  
Raphael T. Haftka
Keyword(s):  
Design Automation ◽  
Sequential Sampling ◽  
Surrogate Modeling ◽  
Computer Experiments ◽  
Limited Resources ◽  
High Fidelity ◽  
Statistical Tools ◽  
Computational Burden ◽  
Intensive Research ◽  
Variable Reduction

Design analysis and optimization based on high-fidelity computer experiments is commonly expensive. Surrogate modeling is often the tool of choice for reducing the computational burden. However, even after years of intensive research, surrogate modeling still involves a struggle to achieve maximum accuracy within limited resources. This work summarizes advanced and yet simple statistical tools that help. We focus on four techniques with increasing popularity in the design automation community: (i) screening and variable reduction in both the input and the output spaces, (ii) simultaneous use of multiple surrogates, (iii) sequential sampling and optimization, and (iv) conservative estimators.

scholarly journals Composite kernel learning

2009 ◽  
Vol 79 (1-2) ◽  
pp. 73-103 ◽  
Author(s):  
Marie Szafranski ◽  
Yves Grandvalet ◽  
Alain Rakotomamonjy
Keyword(s):  
Kernel Learning ◽  
Composite Kernel
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