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6th International Conference on Signal Processing

作者： Pöyhönen, S Negrea, M Arkkio, A Hyötyniemi, H Koivo, H Helsinki Univ Technol Lab Control Engn Helsinki 02015 Finland

ISBN: (纸本)0780374886

support vector classification (SVC) is applied to fault diagnostics of an electrical machine. Numerical magnetic field analysis is used to provide virtual measurement data from healthy and faulty operation of an electrical machine. Power spectra estimates of a stator current of the motor are calculated with Welch's method, and SVC is applied to distinguish healthy spectrum from faulty spectra. Results are promising. Most of the faults can be classified correctly.

关键词： support vector classification fault diagnostics electrical machine finite element analysis

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A Linearly Convergent Linear-Time First-Order Algorithm for support vector classification with a Core Set Result

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INFORMS JOURNAL ON COMPUTING 2011年第3期23卷 377-391页

作者： Kumar, Piyush Yildirim, E. Alper Florida State Univ Dept Comp Sci Tallahassee FL 32306 USA Bilkent Univ Dept Ind Engn TR-06800 Ankara Turkey

We present a simple first-order approximation algorithm for the support vector classification problem. Given a pair of linearly separable data sets and epsilon is an element of(0, 1), the proposed algorithm computes a separating hyperplane whose margin is within a factor of (1-is an element of) of that of the maximum-margin separating hyperplane. We discuss how our algorithm can be extended to nonlinearly separable and inseparable data sets. The running time of our algorithm is linear in the number of data points and in 1/is an element of. In particular, the number of support vectors computed by the algorithm is bounded above by O(zeta/is an element of) for all sufficiently small is an element of > 0, where zeta is the square of the ratio of the distances between the farthest and closest pairs of points in the two data sets. Furthermore, we establish that our algorithm exhibits linear convergence. Our computational experiments, presented in the online supplement, reveal that the proposed algorithm performs quite well on standard data sets in comparison with other first-order algorithms. We adopt the real number model of computation in our analysis.

关键词： support vector machines support vector classification Frank-Wolfe algorithm approximation algorithms core sets linear convergence

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A semismooth Newton method for support vector classification and regression

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2019年第2期73卷 477-508页

作者： Yin, Juan Li, Qingna Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab MCAACI Sch Math & Stat Beijing 100081 Peoples R China

support vector machine is an important and fundamental technique in machine learning. In this paper, we apply a semismooth Newton method to solve two typical SVM models: the L2-loss SVC model and the E-L2-loss SVR model. The semismooth Newton method is widely used in optimization community. A common belief on the semismooth Newton method is its fast convergence rate as well as high computational complexity. Our contribution in this paper is that by exploring the sparse structure of the models, we significantly reduce the computational complexity, meanwhile keeping the quadratic convergence rate. Extensive numerical experiments demonstrate the outstanding performance of the semismooth Newton method, especially for problems with huge size of sample data (for *** problem with 19,996 features and 1,355,191 samples, it only takes 3 s). In particular, for the E-L2-loss SVR model, the semismooth Newton method significantly outperforms the leading solvers including DCD and TRON.

关键词： support vector regression support vector classification Semismooth Newton method Quadratic convergence Generalized Jacobian

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Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

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NEUROCOMPUTING 2015年 168卷 119-127页

作者： Li, Chuan Sanchez, Rene-Vinicio Zurita, Grover Cerrada, Vlariela Cabrera, Diego Vasquez, Rafael E. Chongqing Technol & Business Univ Res Ctr Syst Hlth Maintenance Chongqing 400067 Peoples R China Univ Politecn Salesiana Dept Mech Engn Cuenca Ecuador Univ Pontificia Bolivariana Dept Mech Engn Medellin Colombia

Gearboxes are crucial transmission components in mechanical systems. Fault diagnosis is an important tool to maintain gearboxes in healthy conditions. It is challenging to recognize fault existences and, if any, failure patterns in such transmission elements due to their complicated configurations. This paper addresses a multimodal deep support vector classification (MDSVC) approach, which employs separation-fusion based deep learning in order to perform fault diagnosis tasks for gearboxes. Considering that different modalities can be made to describe same object, multimodal homologous features of the gearbox vibration measurements are first separated in time, frequency and wavelet modalities, respectively. A Gaussian-Bernoulli deep Boltzmann machine (GDBM) without final output is subsequently suggested to learn pattern representations for features in each modality. A support vector classifier is finally applied to fuse GDBMs in different modalities towards the construction of the MDSVC model. With the present model, "deep" representations from "wide" modalities improve fault diagnosis capabilities. Fault diagnosis experiments were carried out to evaluate the proposed method on both spur and helical gearboxes. The proposed model achieves the best fault classification rate in experiments when compared to representative deep and shallow learning methods. Results indicate that the proposed separation-fusion based deep learning strategy is effective for the gearbox fault diagnosis. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Deep learning support vector classification Multimodal homologous feature Gearbox Fault diagnosis

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Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion

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COMPUTATIONAL MECHANICS 2024年第2期74卷 447-466页

作者： Shoghi, Ronak Morand, Lukas Helm, Dirk Hartmaier, Alexander Interdisciplinary Ctr Adv Mat Simulat ICAMS Bochum Germany Fraunhofer Inst Mech Mat IWM Freiburg Germany

In the field of materials engineering, the accurate prediction of material behavior under various loading conditions is crucial. Machine Learning (ML) methods have emerged as promising tools for generating constitutive models straight from data, capable of describing complex material behavior in a more flexible way than classical constitutive models. Yield functions, which serve as foundation of constitutive models for plasticity, can be properly described in a data-oriented manner using ML methods. However, the quality of these descriptions heavily relies on the availability of sufficient high-quality and representative training data that needs to be generated by fundamental numerical simulations, experiments, or a combination of both. The present paper addresses the issue of data selection, by introducing an active learning approach for support vector classification (SVC) and its application in training an ML yield function with suitable data. In this regard, the Query-By-Committee (QBC) algorithm was employed, guiding the selection of new training data points in regions of the feature space where a committee of models shows significant disagreement. This approach resulted in a marked reduction in the variance of model predictions throughout the active learning process. It was also shown that the rate of decrease in the variance went along with an increase in the quality of the trained model, quantified by the Matthews Correlation Coefficient (MCC). This demonstrated the effectiveness of the approach and offered us the possibility to define a dynamic stopping criterion based on the variance in the committee results.

关键词： Data-driven constitutive modeling Machine learning yield function support vector classification Query-by-Committee Optimal training data

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Bilevel hyperparameter optimization for support vector classification: theoretical analysis and a solution method

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MATHEMATICAL METHODS OF OPERATIONS RESEARCH 2022年第3期96卷 315-350页

作者： Li, Qingna Li, Zhen Zemkoho, Alain Beijing Inst Technol Sch Math & Stat Beijing Key Lab MCAACl Key Lab Math Theory & Computat Informat Secur Beijing 100081 Peoples R China Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China Univ Southampton Sch Math Sci Southampton SO17 1BJ Hants England

support vector classification (SVC) is a classical and well-performed learning method for classification problems. A regularization parameter, which significantly affects the classification performance, has to be chosen and this is usually done by the cross-validation procedure. In this paper, we reformulate the hyperparameter selection problem for support vector classification as a bilevel optimization problem in which the upper-level problem minimizes the average number of misclassified data points over all the cross-validation folds, and the lower-level problems are the l(1)-loss SVC problems, with each one for each fold in T-fold cross-validation. The resulting bilevel optimization model is then converted to a mathematical program with equilibrium constraints (MPEC). To solve this MPEC, we propose a global relaxation cross-validation algorithm (GR-CV) based on the well-know Sholtes-type global relaxation method (GRM). It is proven to converge to a C-stationary point. Moreover, we prove that the MPEC-tailored version of the Mangasarian-Fromovitz constraint qualification (MFCQ), which is a key property to guarantee the convergence of the GRM, automatically holds at each feasible point of this MPEC. Extensive numerical results verify the efficiency of the proposed approach. In particular, compared with other methods, our algorithm enjoys superior generalization performance over almost all the data sets used in this paper.

关键词： support vector classification Hyperparameter selection Bilevel optimization Mathematical program with equilibrium constraints C-stationarity

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Prediction of pipe failures in water supply networks using logistic regression and support vector classification

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 196卷 106754-000页

作者： Robles-Velasco, Alicia Cortes, Pablo Munuzuri, Jesus Onieva, Luis Univ Seville ETSI Dept Org Ind & Gest Empresas 2 C Camino de los Descubrimientos S-N Seville 41092 Spain Univ Seville Catedra Agua EMASESA Seville Spain

Companies in charge of water supply networks are making a huge effort to optimally plan the annual replacements of pipes. This would save costs, enable a higher quality of service and a sustainable management of infrastructure. This study presents a methodology to predict pipe failures in water supply networks. Logistic regression and support vector classification are chosen as predictive systems. Both provide a failure probability associated with each sample which is increasingly required by companies that manage these infrastructures. Furthermore, several pre-processing techniques that seek to improve the accuracy of predictions are addressed. The proposed methodology is illustrated with the real case of a Spanish city. This is an extensive water supply network whose recorded data contains 4,393 pipe failures. The results obtained state that the number of unexpected failures might be significantly reduced. Around 30% of failures could have been prevented by replacing only 3% of the networks pipes per year, which is a realistic and feasible option. As a future line of research, the objective must be to develop a global tool that incorporates the failure probability and its consequence, generating the optimal pipe replacement plan.

关键词： Water supply networks Pipe failures support vector classification Logistic regression Predictive algorithms

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Predicting anti-HIV-1 activities of HEPT-analog compounds by using support vector classification

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QSAR & COMBINATORIAL SCIENCE 2005年第9期24卷 1021-1025页

作者： Lu, WC Dong, N Náray-Szabó, G Shanghai Univ Coll Sci Dept Chem Shanghai 200444 Peoples R China Eotvos Lorand Univ Dept Theoret Chem H-1518 Budapest Hungary Eotvos Lorand Univ Hungarian Acad Sci Prot Modelling Grp H-1518 Budapest Hungary

The support vector classification (SVC), as a novel approach, was employed to make a distinction within a class of non-nucleoside reverse transcriptase inhibitors. 1-[2-hydroxyethoxy) methyl]-6-(phenyl thio)-thymine (HEPT) derivatives with high anti-HIV-1 activities and those with low anti-HIV-1 activities were compared on the basis of the following molecular descriptors: net atomic charge on atom 4, molecular volume, partition coefficient, molecular refractivity, molecular polarisability and molecular weight. By using the SVC, a mathematical model was constructed, which can predict the anti-HIV-1 activities of the HEPT-analogue compounds, with an accuracy of 100% as calculated on the basis of the leave-one-out cross-validation (LOOCV) test. The results indicate that the performance of the SVC model exceeds that of the stepwise discriminant analysis (SDA) model, for which a prediction accuracy of 94% was reported.

关键词： HEPT-analogue compounds structure-activity relationship support vector machine support vector classification PM3

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A neural network method for solving support vector classification problems

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NEUROCOMPUTING 2015年 152卷 369-376页

作者： Nazemi, Alireza Dehghan, Mehran Univ Shahrood Sch Math Sci Dept Math Shahrood Iran

This paper presents a recurrent neural network to support vector machine (SVM) learning in pattern classification arising widespread applications in a variety of setting. The SVM learning problem in classification is first converted into an equivalent quadratic programming (QP) formulation, and then a recurrent neural network for SVM learning is proposed. The proposed neural network is guaranteed to obtain the optimal solution of support vector classification. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the QP problem. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Neural network support vector classification Quadratic programming Convergent Stability

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Geological Domaining with Unsupervised Clustering and Ensemble support vector classification

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MINING METALLURGY & EXPLORATION 2023年第6期40卷 2537-2549页

作者： Koruk, Kasimcan Ortiz, Julian M. Gen Directorate Mineral Res & Explorat Dept Feasibil Studies Ankara Turkiye Queens Univ Robert M Buchan Dept Min Kingston ON K7L 3N6 Canada

Building a geological model is important in resource estimation, as it defines the extent of domains for estimation. Geological models are built by assigning a domain to the samples, based on logging of different features related to lithology, alteration, and mineralization. Then, the extent of these domains is determined using geometric or geostatistical tools. However, these models should account for uncertainty, since the actual extent of these domains is not known and must be extrapolated from the labelled samples. With the availability of geochemical datasets and machine learning techniques, employing multiple variables to build resource models should be considered. This article proposes a two-step machine learning approach with an ensemble implementation to define geological domains and their uncertainties. First, an unsupervised binary clustering method labels the geochemical samples into two domains. These labelled samples are used to inform a geological domain model built by support vector classification. This application is repeated with subsets of variables and subsets of samples, leading to an ensemble learning method. Weak models are produced with each subset of samples and variables, and combined to devise a stronger learner. The final model accounts for uncertainties thanks to the ensemble implementation. The proposed workflow is demonstrated on a dataset from a porphyry copper deposit, applied hierarchically to define four domains. Performance is comparable with traditional methods, but provides the advantage of allowing domain knowledge in the selection of the variables, and generates domain boundaries that can be controlled in terms of their continuity and smoothness.

关键词： Machine learning Ensemble learning Geological domaining support vector classification Geostatistics

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