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International Conference on Artificial Intelligence and Signal Processing (AISP)

作者： Mahajan, Puneet Kabbage Inc 1st Floor23 Old Airport Rd Bangalore Karnataka India

ISBN: (纸本)9781728144580;9781728144566

We investigate the potential of using the various kernel functions common to Support Vector Machine(SVM) based modeling from a feature-engineering perspective. Recently, with the advent of Automated Machine Learning, significant research efforts have been placed in areas such as Neural Architecture Search, Hyper Parameter Optimization, Meta Learning, Automated Data Cleaning and Automated Feature Engineering(Auto FE). Auto FE has had some research around automated linear and nonlinear systems feature generation. We have taken a concrete approach towards feature generation taking cue from SVM's kernel tricks which intend to move features from non-linear relationships in lower dimensions to linear relationships in higher dimensions. While the approach was developed for better classification of features in a linear hyperspace, the same technique could be used to assist other classification techniques by auto generating features which add lift to existing feature space. Our approach has had positive uplift for some of the simplistic kernels within SVM literature and has had negative uplift for the more complicated kernel functions. The baseline and experimental tests were done on standard datasets available for research and both datasets were passed through a standard gradient boosting model to handle multi-collinearity.

关键词： automated feature engineering support vector machines kernel functions auto ml

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kernel functions embed into the autoencoder to identify the sparse models of nonlinear dynamics

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2024年 131卷

作者： Dong, Xin Bai, Yu -Long Wan, Wen -Di Northwest Normal Univ Coll Phys & Elect Engn Lanzhou Peoples R China Northwest Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Gansu Peoples R China

Numerous researches have shown that there are three main challenges in data-driven model identification methods: high-dimensional measurements, system complexity and unknown underlying dynamical properties. For most nonlinear dynamics, the feature space defined by the coefficients of their control equations is sparse. Therefore, sparse regression methods are used to learn the sparse coefficients of the control equations of nonlinear dynamics. However, this method strongly depends on the appropriate selection of the sparse basis vectors. In this essay, the autoencoder is combined with the sparse regression method to simultaneously identify the sparse coordinate and a parsimonious, interpretable and generalizable model of the specified system. It also integrates kernel functions to map the intractable measurements in the hidden space of the autoencoder into a linearly distinguishable kernel space, which kernelizes the candidate function library of the sparse identification of nonlinear dynamics (SINDy) model as the sparse dictionaries. Therefore, the flexible representation of neural networks, the simplicity of sparse regression methods and the implicit non-linear representation of kernel functions are consolidated in this article. To inspect the reliability of the proposed model in this paper, a set of nonlinear dynamics formulated by ordinary differential equations (ODEs), second-order trigonometric functions and partial differential equations (PDEs) are utilized as test cases. And the comparisons between the proposed model and other model identification methods illustrate that the performance of the former is the best.

关键词： Model identification SINDy kernel functions Autoencoder

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Lymph diseases diagnosis approach based on support vector machines with different kernel functions 9

Lymph diseases diagnosis approach based on support vector ma...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Elshazly, Hanaa Ismail Elkorany, Abeer Mohamed Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt Sci Res Grp Egypt Giza Egypt

ISBN: (纸本)9781479965946

In this paper, a Genetic algorithm (GA) hased supporting vector machine classifier (GA-SVM) is proposed for lymph diseases diagnosis. In the first stage, dimension of lymph diseases dataset that has 18 features is reduced to six features using GA. In the second stage, a support vector machine with different kernel functions including linear, Quadratic and Gaussian was utilized as a classifier. The Lymphography database was obtained from the University Medical Center, Institute of Oncology, Ljubljana, Yugoslavia. The obtained classification accuracy was very promising with regard to the other classification applications in the literature for this problem. The performance of SVM classifier with each kernel function was evaluated by using performance indices such as accuracy, sensitivity, specificity, area under curve (AUC) or (ROC), Matthews Correlation Coefficient (MCC) and F-Measure. Linear kernel function obtained highest results which verifies the efficiency of GA-linear stategy.

关键词： diseases genetic algorithms medical diagnostic computing patient diagnosis pattern classification support vector machines GA based supporting vector machine classifier GA-SVM GA-linear stategy Gaussian kernel function Institute of Oncology Ljubljana Lymphography database SVM classifier performance University Medical Center Yugoslavia classification accuracy classification applications genetic algorithm linear kernel function lymph disease dataset lymph disease diagnosis approach performance indices quadratic kernel function Algorithm design and analysis Classification algorithms Extraterrestrial measurements Indexes kernel Prediction algorithms Support vector machines Support Vector Network kernel functions Genetic Algorithm (GA) medical diagnostic computing Extraterrestrial measurements Algorithm design and analysis Academic Medical Centers performance indicators patient diagnosis Prediction algorithms Sorting algorithm Pattern recognition Disease diagnosis kernel maladjustment

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Weighted kernel functions for SVM learning in string domains - A distance function viewpoint

Weighted kernel functions for SVM learning in string domains...

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4th International Conference on Machine Learning and Cybernetics

作者： Vanschoenwinkel, B Liu, F Manderick, B Vrije Univ Brussels Computat Modeling Lab Dept Informat B-1050 Brussels Belgium

ISBN: (纸本)0780390911

This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, context-based representation of the data should be preferred over a continuous, real format as this is a much more intuitive setting for working with (weighted) distance functions. It is shown how a weighted string distance can be decomposed and subsequently used in different kernel functions and how these kernel functions correspond to inner products between real vectors. As a case-study named entity recognition is used with information gain ratio as a weighting scheme.

关键词： support vector machines kernel functions metrics information gain ratio named entity recognition

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Relation Extraction - A Study on kernel functions 23

Relation Extraction - A Study on Kernel Functions

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23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Awny, Deena Johannes Kepler Univ Linz Linz Austria

ISBN: (纸本)9781665406505

This paper presents a study on one of the important methods for the relation extraction problem. Roughly, the problem of relation extraction is the problem of finding all (some) facts in a given natural language sentence. The method is based on kernel functions for the support vector machines (SVM) algorithm. A kernel function is a function that represents the similarity of semantics between sentences. The kernel functions from the literature considered, in this paper, to be the most important ones are: bag-of-words kernel (BOWK), global context kernel (GCK), and sub-tree kernel (STK). The SVM algorithm is utilized to learn a candidate sentence classification model for a target relation r. The classification model can then be used to determine whether a new candidate sentence (with two labeled object descriptions) is a positive or a negative candidate sentence with respect to the given relation r. The paper includes a systematic comparison of the various kernel functions proposed in the literature. Based on the strength and weaknesses of the various kernel functions, new kernel functions are proposed, namely, directed global context kernel (DGCK), global context synonyms kernel (GCSK), directed global context synonyms kernel (DGCSK), and sub-tree synonyms kernel (STSK). Lastly, the paper contains a comparison of the evaluation results of the new kernel functions with the results of the kernel functions proposed in the literature.

关键词： relation extraction support vector machines kernel functions

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Investigation on Different kernel functions for Weighted kernel Regression in Solving Small Sample Problems

Investigation on Different Kernel Functions for Weighted Ker...

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UKSim 5th European Modelling Symposium on Computer Modelling and Simulation (EMS)

作者： Shapiai, Mohd Ibrahim Sudin, Shahdan Ibrahim, Zuwairie Khalid, Marzuki Univ Teknol Malaysia Ctr Artificial Intelligent & Robot CAIRO Kuala Lumpur 54100 Malaysia

ISBN: (纸本)9780769546193

Previously, weighted kernel regression (WKR) has proved to solve small problems. The existing WKR has been successfully solved rational functions with very few samples. The design and development of WKR is important in order to extend the capability of the technique with various kernel functions. Based on WKR, a simple iteration technique is employed to estimate the weight parameters with Gaussian as a kernel function before WKR can be used in predicting the unseen test samples. In this paper, however, we investigate various kernel functions with Particle Swarm Optimization (PSO) as weight estimators as it offers such flexibility in defining the objective function. Hence, PSO has the capability to solve non-closed form solution problem as we also introduce regularization term with L-1 norm in defining the objective function as to solve training sample, which corrupted by noise. Through a number of computational experiments, the investigation results show that the prediction quality of WKR is primarily dominated by the smoothing parameter selection rather than the type of kernel function.

关键词： Weighted kernel regression (WKR) particle swarm optimization (PSO) small samples kernel functions

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Evolving Sum and Composite kernel functions for Regularization Networks

Evolving Sum and Composite Kernel Functions for Regularizati...

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10th International Conference on Adaptive and Natural Computing Algorithms

作者： Vidnerova, Petra Neruda, Roman Acad Sci Czech Republic Inst Comp Sci Prague 8 Czech Republic

ISBN: (纸本)9783642202810

In this paper we propose a novel evolutionary algorithm for regularization networks. The main drawback of regularization networks in practical applications is the presence of meta-parameters, including the type and parameters of kernel functions Our learning algorithm provides a solution to this problem by searching through a space of different kernel functions, including sum and composite kernels. Thus, an optimal combination of kernel functions with parameters is evolved for given task specified by training data. Comparisons of composite kernels, single kernels, and traditional Gaussians are provided in several experiments.

关键词： regularization networks kernel functions genetic algorithms

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Comparing the Performance of the kernel functions in the LDA-SVM Based Classification Algorithm in the LabVIEW Environment

Comparing the Performance of the Kernel Functions in the LDA...

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International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Kaya, Duygu Turk, Mustafa Firat Univ Dept Elect & Elect Engn Fac Engn Elazig Turkey

ISBN: (纸本)9781538668788

Machine learning, which is a paradigm of an unknown method that makes inferences of existing data by mathematical and statistical methods, consists of different algorithm choices according to the structure of the used data. Machine learning consists of supervised-unsupervised learning algorithms and dimension reduction algorithms with advantages and disadvantages within themselves. Linear Discriminant Analysis (LDA) is the most commonly used as dimensionality reduction technique for pattern recognition and machine learning applications. Also, Support Vector Machine (SVM) is one of the most used supervised algorithms because of its ability to separate data from different classes from each other by a remote hyperplane, high performance, easy to implement and high generalization capability. In this paper, the dimension of breast cancer data was reduced by LDA and the obtained feature data was classified by using SVM. In order to compare the performance of LabVIEW with MATLAB, data reduced in dimension in LabVIEW and feature data is classified with codes written in the MATLAB and LabVIEW. Then, model performance results are analyzed with LabVIEW. In SVM, Gaussian and Polynomial kernel functions are used and obtained results are compared with each other.

关键词： LabVIEW Linear Discriminant Analysis Support Vector Machine kernel functions

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Identification of linear non-viscous damping with different kernel functions in the time domain

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JOURNAL OF SOUND AND VIBRATION 2020年 487卷 115623-115623页

作者： Shen, Renjie Qian, Xiangdong Zhou, Jianfang Hohai Univ Coll Mech & Mat 1Xikang Rd Nanjing 210098 Jiangsu Peoples R China

The non-viscous damping model considers the complete velocity history via a convolution over the kernel functions. In this paper, a procedure is proposed for identifying a damping model involving different kernel functions in the time domain. The method uses responses calculated by the generic time-domain integration with the known mass matrix and the stiffness matrix. The relaxation parameter in the kernel function is identified through a summation of the error function, and the damping coefficient matrix is identified via the linear least squares method. Two examples show that reasonable estimates could be obtained by the proposed method. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Non-viscous damping Time domain identification Relaxation parameter Damping coefficient matrix kernel functions

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A novel kernel functions algorithm for solving impulsive boundary value problems

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APPLIED MATHEMATICS LETTERS 2022年 134卷

作者： Geng, F. Z. Wu, X. Y. Changshu Inst Technol Dept Math Changshu 215500 Jiangsu Peoples R China Nanjing Univ Dept Math Nanjing 210093 Jiangsu Peoples R China Zaozhuang Univ Sch Math & Stat Zaozhuang 277160 Peoples R China

In this letter, to fit the character of solutions to impulsive boundary value problems (IBVPs), we firstly construct a piecewise continuous space using the reproducing kernel function (RKF) of smooth reproducing kernel Hilbert space (RKHS) W3. Then we present a fourth order convergent collocation approach for IBVPs by using the piecewise continuous basis functions yielded by RKFs in W3. Also, the convergence order of our approach is proved. The accuracy and convergence order are shown numerically.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Impulsive boundary value problems Piecewise continuous space kernel functions

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