Composite Kernel Functions for Surrogate Modeling using Recursive Multi-Fidelity Kriging

2022 ◽  
Author(s):  
Pramudita S. Palar ◽  
Lucia Parussini ◽  
Luigi Bregant ◽  
Koji Shimoyama ◽  
Muhammad F. Izzaturrahman ◽  
...  
Keyword(s):  
Surrogate Modeling ◽  
Kernel Functions ◽  
Composite Kernel

scholarly journals Assimilation of Standard Regularizer Contextual Model and Composite Kernel with Fuzzy-based Noise Classifier

2019 ◽  
Vol 11 (1) ◽  
pp. 16-24
Author(s):  
Ishuita SenGupta ◽  
Anil Kumar ◽  
Rakesh Kumar Dwivedi
Keyword(s):  
Remote Sensing ◽  
Comparative Analysis ◽  
Classification Accuracy ◽  
Remote Sensing Data ◽  
Kernel Functions ◽  
Contextual Model ◽  
Markov Random ◽  
Composite Kernel ◽  
Kernel Composition ◽  
Standard Regularization

The paper assay the effect of assimilating smoothness prior contextual model and composite kernel function with fuzzy based noise classifier using remote sensing data. The concept of the composite kernel has been taken by fusing two kernels together to improve the classification accuracy. Gaussian and Sigmoid kernel functions have opted for kernel composition. As a contextual model, Markov Random Field (MRF) Standard regularization model (smoothness prior) has been studied with the composite kernel-based Noise Classifier. Comparative analysis of new classifier with the conventional construes increase in overall accuracy.

scholarly journals Dependent-Gaussian-Process-Based Learning of Joint Torques Using Wearable Smart Shoes for Exoskeleton

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3685
Author(s):  
Jiantao Yang ◽  
Yuehong Yin
Keyword(s):  
Gaussian Process ◽  
Learning Algorithm ◽  
Low Cost ◽  
Joint Kinematics ◽  
Kernel Functions ◽  
Lower Limbs ◽  
Joint Torque ◽  
Human Gait ◽  
Joint Torques ◽  
Composite Kernel

Estimating the joint torques of lower limbs in human gait is a highly challenging task and of great significance in developing high-level controllers for lower-limb exoskeletons. This paper presents a dependent Gaussian process (DGP)-based learning algorithm for joint-torque estimations with measurements from wearable smart shoes. The DGP was established to perform data fusion, and serves as the mathematical foundation to explore the correlations between joint kinematics and joint torques that are embedded deeply in the data. As joint kinematics are used in the training phase rather than the prediction process, the DGP model can realize accurate predictions in outdoor activities by using only the smart shoe, which is low-cost, nonintrusive for human gait, and comfortable to wearers. The design methodology of dynamic specific kernel functions is presented in accordance to prior knowledge of the measured signals. The designed composite kernel functions can be used to model multiple features at different scales, and cope with the temporal evolution of human gait. The statistical nature of the proposed DGP model and the composite kernel functions offer superior flexibility for time-varying gait-pattern learning, and enable accurate joint-torque estimations. Experiments were conducted with five subjects, whose results showed that it is possible to estimate joint torques under different trained and untrained speed levels. Comparisons were made between the proposed DGP and Gaussian process (GP) models. Obvious improvements were achieved when all DGP r2 values were higher than those of GP.

scholarly journals Multiple Kernel-Based SVM Classification of Hyperspectral Images by Combining Spectral, Spatial, and Semantic Information

2020 ◽  
Vol 12 (1) ◽  
pp. 120 ◽  
Author(s):  
Yi Wang ◽  
Wenke Yu ◽  
Zhice Fang
Keyword(s):  
Semantic Information ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Texture Features ◽  
Kernel Functions ◽  
Support Vector ◽  
Multiple Kernel ◽  
Gabor Features ◽  
Composite Kernel

In this study, we present a hyperspectral image classification method by combining spectral, spatial, and semantic information. The main steps of the proposed method are summarized as follows: First, principal component analysis transform is conducted on an original image to produce its extended morphological profile, Gabor features, and superpixel-based segmentation map. To model spatial information, the extended morphological profile and Gabor features are used to represent structure and texture features, respectively. Moreover, the mean filtering is performed within each superpixel to maintain the homogeneity of the spatial features. Then, the k-means clustering and the entropy rate superpixel segmentation are combined to produce semantic feature vectors by using a bag of visual-words model for each superpixel. Next, three kernel functions are constructed to describe the spectral, spatial, and semantic information, respectively. Finally, the composite kernel technique is used to fuse all the features into a multiple kernel function that is fed into a support vector machine classifier to produce a final classification map. Experiments demonstrate that the proposed method is superior to the most popular kernel-based classification methods in terms of both visual inspection and quantitative analysis, even if only very limited training samples are available.

A Composite Kernel for Word Sense Disambiguation

2014 ◽  
Vol 530-531 ◽  
pp. 522-525
Author(s):  
Ting Hua Wang ◽  
Wen Sheng Zhu ◽  
Qiong Zhang ◽  
Hai Hui Xie
Keyword(s):  
Word Sense Disambiguation ◽  
Empirical Evaluation ◽  
Ambiguous Word ◽  
Kernel Functions ◽  
Learning Approaches ◽  
Word Sense ◽  
Sources Of Information ◽  
Composite Kernel ◽  
Sense Disambiguation ◽  
Kernel Combination

The success of supervised learning approaches to word sensed disambiguation (WSD) is largely dependent on the representation of the context in which an ambiguous word occurs. In practice, different kernel functions can be designed according to different representations since kernels can be well defined on general types of data, such as vectors, sequences, trees, as well as graphs. In this paper, we present a composite kernel, which is a linear combination of two types of kernels, i.e., bag of words (BOW) kernel and sequence kernel, for WSD. The benefit of kernel combination is that it allows to integrate heterogeneous sources of information in a simple and effective way. Empirical evaluation shows that the composite kernel can consistently improve the performance of WSD.

Modified algorithm for fast bandwidth selection for kernel estimates of multidimensional probability densities

2020 ◽  
pp. 9-13
Author(s):  
A. V. Lapko ◽  
V. A. Lapko
Keyword(s):  
Probability Density ◽  
Optimal Parameter ◽  
Random Variables ◽  
Kernel Functions ◽  
Independent Random Variables ◽  
Functional Dependencies ◽  
Approximation Properties ◽  
Probability Density Estimation ◽  
Multidimensional Probability ◽  
Selection Of

An original technique has been justified for the fast bandwidths selection of kernel functions in a nonparametric estimate of the multidimensional probability density of the Rosenblatt–Parzen type. The proposed method makes it possible to significantly increase the computational efficiency of the optimization procedure for kernel probability density estimates in the conditions of large-volume statistical data in comparison with traditional approaches. The basis of the proposed approach is the analysis of the optimal parameter formula for the bandwidths of a multidimensional kernel probability density estimate. Dependencies between the nonlinear functional on the probability density and its derivatives up to the second order inclusive of the antikurtosis coefficients of random variables are found. The bandwidths for each random variable are represented as the product of an undefined parameter and their mean square deviation. The influence of the error in restoring the established functional dependencies on the approximation properties of the kernel probability density estimation is determined. The obtained results are implemented as a method of synthesis and analysis of a fast bandwidths selection of the kernel estimation of the two-dimensional probability density of independent random variables. This method uses data on the quantitative characteristics of a family of lognormal distribution laws.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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