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kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 714卷 136765页

作者： Chen, Huazhou Xu, Lili Ai, Wu Lin, Bin Feng, Quanxi Cai, Ken Guilin Univ Technol Coll Sci Guilin 541004 Peoples R China Guilin Univ Technol Ctr Data Anal & Algorithm Technol Guilin 541004 Peoples R China Beibu Gulf Univ Coll Marine Sci Qinzhou 535011 Peoples R China Zhongkai Univ Agr & Engn Coll Automat Zhongkai Rd 501 Guangzhou 510225 Peoples R China

Water pollution is a challenging problem encountered in total environmental development. Near-infrared (NIR) spectroscopy is a well-refined technology for rapid water pollution detection. Calibration models are established and optimized to search for chemometric algorithms with considerably improved prediction effects. Machine learning improves the prediction capability of NIR spectroscopy for the accurate assessment of water pollution. Least squares support vector machine (LSSVM) algorithm fits parameters to target problems in a data-driven manner. The modeling capability of this algorithm mainly depends on its kernel functions. In this study, the LSSVM method was used to establish NIR calibration models for the quantitative determination of chemical oxygen demand, which is a critical indicator of water pollution level. The effects of different kernels embedded in LSSVM were investigated. A novel kernel was proposed by using a logistic-based neural network. In contrast to common kernels, this novel kernel can utilize a deep learning approach for parameter optimization. The proposed kernel also strengthens model resistance to over-fitting such that cross-validation can be reasonably utilized. The proposed novel kernel is applicable for the quantitative determination of water pollution and is a prospective solution to other problems in the field of water resource management. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Water pollution Near-infrared spectroscopy Least squares support vector machine Logistic-based network kernel functions

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kernel functions for case-based planning

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ARTIFICIAL INTELLIGENCE 2010年第16-17期174卷 1369-1406页

作者： Serina, Ivan Free Univ Bozen Bolzano I-39042 Bressanone Italy

Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning problems in the future. Although comparative worst-case complexity analyses of plan generation and reuse techniques reveal that it is not possible to achieve provable efficiency gain of reuse over generation, we show that the case-based planning approach can be an effective alternative to plan generation when similar reuse candidates can be chosen. In this paper we describe an innovative case-based planning system, called OAKPLAN, which can efficiently retrieve planning cases from plan libraries containing more than ten thousand cases, choose heuristically a suitable candidate and adapt it to provide a good quality solution plan which is similar to the one retrieved from the case library. Given a planning problem we encode it as a compact graph structure, that we call Planning Encoding Graph, which gives us a detailed description of the topology of the planning problem. By using this graph representation, we examine an approximate retrieval procedure based on kernel functions that effectively match planning instances, achieving extremely good performance in standard benchmark domains. The experimental results point out the effect of the case base size and the importance of accurate matching functions for global system performance. Overall, we show that OAKPLAN is competitive with state-of-the-art plan generation systems in terms of number of problems solved, CPU time, plan difference values and plan quality when cases similar to the current planning problem are available in the plan library. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Case-based planning Domain-independent planning Case-based reasoning Heuristic search for planning kernel functions

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kernel functions for Categorical Variables with Application to Problems in the Life Sciences

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16th International Conference of the Catalan-Association-of-Artificial-Intelligence (CCIA)

作者： Belanche, Lluis A. Villegas, Marco A. Tech Univ Catalonia Dept Software Sch Comp Sci Barcelona 08034 Spain

ISBN: (纸本)9781614993209;9781614993193

We introduce a family of positive definite kernels specifically designed for problems described by categorical information. The kernels are based on the comparison of the probability mass function of the variables and have a clear interpretation in terms of similarity computations between the modalities. We report experimental results on two different problems in the life sciences indicating that the proposed approach may markedly outperform standard kernels, so it can be used as a good alternative to other common kernel functions (at least for SVM classification) in order to obtain better accuracy.

关键词： kernel functions categorical variables support vector machines

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Superconvergent kernel functions approaches for the second kind Fredholm integral equations

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APPLIED NUMERICAL MATHEMATICS 2021年 167卷 202-210页

作者： Li, X. Y. Wu, B. Y. Changshu Inst Technol Sch Math & Stat Suzhou 215500 Jiangsu Peoples R China Univ S Florida Sch Math & Stat Tampa FL 33620 USA Harbin Inst Technol Sch Math Sci Harbin 150001 Heilongjiang Peoples R China

By employing the kernel functions with the form of piecewise polynomials in the Sobolev reproducing kernel Hilbert spaces (RKHSs), a globally superconvergent numerical technique is proposed to solve the second kind linear integral equations of Fredholm type. This method has an order of global convergence 0 (h(4)) and 0 (h(6)) based on the kernel functions in the Sobolev RKHSs H-1 and H-2, respectively. Three linear Fredholm integral equations, one Volterra-Fredholm integral equation and one nonlinear Fredholm integral equation are numerically solved by the present approach to verify the superconvergence and effectiveness. (C) 2021 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： kernel functions Fredholm integral equations Superconvergence order

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Comparative Assessment of Improved SVM Method under Different kernel functions for Predicting Multi-scale Drought Index

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WATER RESOURCES MANAGEMENT 2023年第3期37卷 1367-1399页

作者： Pande, Chaitanya B. Kushwaha, N. L. Orimoloye, Israel R. Kumar, Rohitashw Abdo, Hazem Ghassan Tolche, Abebe Debele Elbeltagi, Ahmed Indian Inst Trop Meteorol Pune India ICAR Indian Agr Res Inst Div Agr Engn Pusa Campus New Delhi 110012 India Univ Free State Ctr Environm Management Bloemfontein South Africa Univ West Indies Fac Food Agr Dept Geog St Augustine Campus St Augustine Trinidad Tobago SKUAST Kashmir Coll Agr Engn & Technol Srinagar 190025 Jammu & Kashmir India Univ Tartous Fac Arts & Humanities Geog Dept Tartous Syria Haramaya Univ Haramaya Inst Technol Sch Water Resources & Environm Engn Dire Dawa Ethiopia Mansoura Univ Fac Agr Agr Engn Dept Mansoura 35516 Egypt Univ Tenaga Nas Inst Energy Infrastruct Kajang 43000 Malaysia Damascus Univ Fac Arts & Humanities Geog Dept Damascus Syria

This paper focus on the drought monitoring and forecasting for semi-arid region based on the various machine learning models and SPI index. Drought phenomena are crucial role in the agriculture and drinking purposes in the area. In this study, Standardized Precipitation Index (SPI) was used to predicted the future drought in the upper Godavari River basin, India. We have selected the ten input combinations of ML model were used to prediction of drought for three SPI timescales (i.e., SPI -3, SPI-6, and SPI-12). The historical data of SPI from 2000 to 2019 was used for creation of ML models SPI prediction, these datasets was divided into training (75% of the data) and testing (25% of the data) models. The best subset regression method and sensitivity analysis were applied to estimate the most effective input variables for estimation of SPI 3, 6, and 12. The improved support vector machine model using sequential minimal optimization (SVM-SMO) with various kernel functions i.e., SMO-SVM poly kernel, SMO-SVM Normalized poly kernel, SMO-SVM PUK (Pearson Universal kernel) and SMO-SVM RBF (radial basis function) kernel was developed to forecasting of the SPI-3,6 and 12 months. The ML models accuracy were compared with various statistical indicators i.e., root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), root relative squared error (RRSE), and correlation coefficient (r). The results of study area have been showed that the SMO-SVM poly kernel model precisely predicted the SPI-3 (R-2 = 0.819) and SPI-12 (R-2 = 0.968) values at Paithan station;the SPI-3 (R-2 = 0.736) and SPI-6 (R-2 = 0.841) values at Silload station, respectively. The SMO-SVM PUK kernel is found that the best ML model for the prediction of SPI-6 (R-2 = 0.846) at Paithan station and SPI-12 (R-2 = 0.975) at the Silload station. The compared with SVM-SMO poly kernel and SVM-SMO PUK kernel was observed, these models are best forecasting of drought (i.e. SPI-6 and SPI-12), wh

关键词： SPI Sensitivity analysis Support vector machine Best subset regression kernel functions

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Annual and seasonal cycles of CO2 and CH4 in a Mediterranean Spanish environment using different kernel functions

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2019年第3期33卷 915-930页

作者： Fernandez-Duque, Beatriz Perez, Isidro A. Angeles Garcia, M. Pardo, Nuria Luisa Sanchez, M. Univ Valladolid Dept Appl Phys Fac Sci Paseo Belen 7 E-47011 Valladolid Spain

This paper is based on CO2 and CH4 semi-hourly mole fraction measurements obtained at the Low Atmosphere Research Centre (CIB) between 2010 and 2016 using a Picarro G1301 analyser. The main aims of the study were to examine the temporal variation of CO2 and CH4 by using six different kernel functions, and to study the suitability of these functions to the dataset. The method used for the current study was based on experimental contour plots of R-2 values in order to simultaneously determine the bandwidths of kernel functions for the long-term and short-term. An Epanechnikov, a Gaussian, a biweight, a triangular, a tricubic and a rectangular kernel function were applied to extract the salient features of both the long-term (trend) and the short-term (seasonality). The average linear increase growth rates found were mainly attributed to the terrestrial biosphere cycle and changes in the atmospheric circulation regime. The seasonal cycle exhibited a cyclical variation, revealing summer minima for both gases, which may be explained by a biological minimum. kernel analysis showed two nocturnal CO2 maxima, in spring and autumn, linked to an increase in rainfall. For CO2 daytime records, only the spring peak was detected. As regards CH4, the maximum was located in winter. The best fit for the trend was obtained by the biweight kernel. In contrast, the best adjustment for seasonality was achieved from the Gaussian and the triangular kernel. To sum up, optimal bandwidth selection is important when kernel regression functions are employed. Since no important differences were found between the kernels employed, those which involve least computational effort are recommended.

关键词： Trend Seasonal cycle Rural kernel functions Bandwidth Contour plot

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Nonlinear feature extraction based on centroids and kernel functions

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PATTERN RECOGNITION 2004年第4期37卷 801-810页

作者： Park, CH Park, H Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

A nonlinear feature extraction method is presented which can reduce the data dimension down to the number of classes, providing dramatic savings in computational costs. The dimension reducing nonlinear transformation is obtained by implicitly mapping the input data into a feature space using a kernel function, and then finding a linear mapping based on an orthonormal basis of centroids in the feature space that maximally separates the between-class relationship. The experimental results demonstrate that our method is capable of extracting nonlinear features effectively so that competitive performance of classification can be obtained with linear classifiers in the dimension reduced space. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： cluster structure dimension reduction kernel functions kernel orthogonal centroid method linear discriminant analysis nonlinear feature extraction pattern classification support vector machines

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Wind power prediction method based on regime of switching kernel functions

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JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS 2016年 153卷 26-33页

作者： Ouyang, Tinghui Zha, Xiaoming Qin, Liang Xiong, Yi Xia, Tian Wuhan Univ Sch Elect Engn Wuhan 430072 Peoples R China

The fluctuation of wind cause threat to power grid, this paper proposed a wind power prediction method to improving this situation. The proposed method is based on time series and regime of switching kernel functions. First, the mutual information method and the false nearest neighbor method were used to calculate parameters to reconstruct the original data. The recurrence figure and the Lyapunov exponent were applied to verify that the time series data was from a chaotic system. Then, this paper proposed a prediction method based on the kernel function and also a switching regime based on the support vectors machine. The new prediction method combining these two parts was proposed to predict wind power. The comparison of wind power prediction by the proposed method and traditional methods were present, the results validated that the proposed method is feasible to predict wind power, and that the precision of prediction is improved, which will be useful for the future analysis of wind power. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Wind power prediction Time series kernel functions Support vector machine Switching regime

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Inducing Contextual Classifications With kernel functions Into Support Vector Machines

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2018年第6期15卷 962-966页

作者： Negri, Rogerio Galante da Silva, Erivaldo Antonio Casaca, Wallace UNESP Inst Ciencia & Tecnol BR-12247004 Sao Jose Dos Campos Brazil UNESP Fac Ciencia & Tecnol BR-19060900 Presidente Prudente Brazil Univ Estadual Paulista Campus Expt Rosana BR-19274000 Sao Paulo Brazil

kernel functions have revolutionized theory and practice in the field of pattern recognition, especially to perform image classification. Besides giving rise to nonlinear variants of the well-known support vector machine (SVM), these functions have also been successfully used to classify nonvectorial data (e.g., graphs and collection of sets), in which customized metrics are created to precisely measure the similarity among such contextual data entities. This letter introduces two cantext-inspired kernel functions as new SVM-driven methods for remote sensing image classification. In contrast to the existing SVM-based approaches that assume only multiattribute vectors as representative features in a high-dimensional space, the proposed models formally establish comparisons between the entire sets of context-given data, thus employing these contextual measurements to drive the classification. More precisely, stochastic distances as well as hypothesis tests are conveniently handled and "kernelized" to build our models. A complete battery of experiments involving both remote sensing and real-world images is conducted to validate the performance of the proposed kernels against various well-established SVM-based methods.

关键词： Context image classification kernel functions

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Integrating regression formulas and kernel functions into locally adaptive knowledge-based neural networks: A case study on renal function evaluation

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2006年第3期36卷 235-244页

作者： Song, Q Kasabov, N Ma, TM Marshall, MR Univ Auckland Knowledge Engn & Discovery Res Inst Auckland 1020 New Zealand Middlemore Hosp Dept Renal Med Auckland 6 New Zealand

Objective: In many medical areas, there exist different regression formulas to predict/evaluate a medical outcome on the same problem, each of them being efficient only in a particular sub-space of the problem space. The paper aims at the development of a generic, incremental Learning model that includes all available regression formulas for a particular prediction problem to define local areas of the problem space with their best performing formula along with useful explanation rules. Another objective of the paper is to develop a specific model for renal function evaluation using nine existing formulas. Methods and materials: We have used a connectionist neuro-fuzzy approach and have developed a knowledge-based neural network model (KBNN) which incorporates and adapts incrementally several existing regression formulas and kernel functions. The model incorporates different non-linear regression functions as neurons in its hidden layer and adapts these functions through incremental learning from data in particular local areas of the space. More specifically, each hidden neural node has a pair of functions associated with it-one regression formula, that represents existing knowledge and one Gaussian kernel function, that defines the sub-space of the whole problem space, in which the formula is Locally adapted to new data. All these functions are aggregated and changed through incremental learning. The proposed KBNN model is illustrated using a medical dataset of observed patient glomerular filtration rate (GFR) measurements for renal function evaluation. In this case study, the regression function for each cluster is selected by the model from nine formulas commonly used by medical practitioners to predict GFR. 441 GFR data vectors from 141 patients taken from 12 sites in Australia and New Zealand have been used as a case study experimental data set. Results: The proposed GFR prediction model, based on the proposed generic KBNN model, outperforms at least by 10% accu

关键词： knowledge-based neural networks kernel functions glomerular filtration rate renal function evaluation

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