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Distributed Inference for linear support vector machine

JOURNAL OF machine LEARNING RESEARCH 2019年第1期20卷 1-41页

作者： Wang, Xiaozhou Yang, Zhuoyi Chen, Xi Liu, Weidong Shanghai Jiao Tong Univ Sch Math Sci Shanghai 200240 Peoples R China NYU Stern Sch Business New York NY 10012 USA Shanghai Jiao Tong Univ Sch Math Sci Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China

The growing size of modern data brings many new challenges to existing statistical inference methodologies and theories, and calls for the development of distributed inferential approaches. This paper studies distributed inference for linear support vector machine (SVM) for the binary classification task. Despite a vast literature on SVM, much less is known about the inferential properties of SVM, especially in a distributed setting. In this paper, we propose a multi-round distributed linear-type (MDL) estimator for conducting inference for linear SVM. The proposed estimator is computationally efficient. In particular, it only requires an initial SVM estimator and then successively refines the estimator by solving simple weighted least squares problem. Theoretically, we establish the Bahadur representation of the estimator. Based on the representation, the asymptotic normality is further derived, which shows that the MDL estimator achieves the optimal statistical efficiency, i.e., the same efficiency as the classical linear SVM applying to the entire data set in a single machine setup. Moreover, our asymptotic result avoids the condition on the number of machines or data batches, which is commonly assumed in distributed estimation literature, and allows the case of diverging dimension. We provide simulation studies to demonstrate the performance of the proposed MDL estimator.

关键词： linear support vector machine distributed inference Bahadur representation asymptotic theory

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linear and non-linear proximal support vector machine classifiers for wind speed prediction

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 379-390页

作者： Ranganayaki, V. Deepa, S. N. Dr NGP Inst Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India Anna Univ Dept Elect & Elect Engn Reg Campus Coimbatore Tamil Nadu India

The focus is made to develop predictor models for wind speed prediction employing the support vector machine neural models. Basically, support vector machines (SVM) is employed as classifiers, but this contribution models variant of SVM to act as predictors. A developed model of linear support vector machine (LSVM) and proximal support vector machine (PSVM) is proposed to carry out the wind speed prediction using the available real time wind farm data. In developed PSVM predictor, it is modeled for both linear PSVM predictor and non-linear PSVM predictor. The difference between the developed linear and non-linear PSVM predictor models lies in their applicability of kernel functions to perform effective wind speed prediction. The prediction application is implemented for the set of wind farm data with a wind mill height of 50 m in a manner to minimize the mean square error. The training process of the neural network algorithmic flow is done with the developed LSVM, L-PSVM (linear PSVM) and N-PSVM (nonlinear PSVM) for predicting the wind speed in renewable energy systems. Results computed are compared with the other types of predictors to prove the effectiveness of the proposed variants of SVM predictors. The simulated results presents the effectiveness of the proposed predictors for the real time wind farm data and the applicability of the predictors for the considered datasets.

关键词： linear support vector machine Proximal support vector machine Mean square Error Wind speed prediction

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A sensor-based golfer-swing signature recognition method using linear support vector machine

ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW

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ELEKTROTEHNISKI VESTNIK-ELECTROCHEMICAL REVIEW 2017年第5期84卷 247-252页

作者： Zhang, Zhichao Zhang, Yuan Kos, Anton Umek, Anton Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Shandong Peoples R China Univ Ljubljana Fac Elect Engn Ljubljana Slovenia

Golf performance varies from person to person because of the differences in physical features of golfer's body and skill level. Recognizing golf swings of an individual golf player is essential to help improving the golf skill level. This can be done through feedback information provided by a specialized equipment or by a personal coach. Based on the classification of the golfer-swing shapes, this work analyses golf-swings. A sensor-based golfer-swing signature-recognition method is performed by using linear support vector machine (LSVM). Golf-swing signals are acquired by a strain-gage sensor fitted to the golf club that measures the club bend. To classify each golfer-swing multi-class classifier is built by combining binary LSVM models with an error-correcting-output-codes multi-class strategy. The experiment results of the training accuracy, testing accuracy and training time are compared with the results of other models including decision-tree algorithms, discriminant-analysis algorithms, other support vector machine algorithms, k-nearest neighbor (KNN) classifiers, and ensemble classifiers. A comparison shows that by using the strain-gage sensor and multi-class LSVM model, the golfer-swing signature is recognized accurately and effectively.

关键词： strain-gage sensor golf swing golfer-swing signature recognition linear support vector machine

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Exploiting linear support vector machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks

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SENSORS 2018年第9期18卷 2840-2840页

作者： Muriira, Lawrence Mwenda Zhao, Zhiwei Min, Geyong Univ Elect Sci & Technol China Coll Comp Sci & Engn Chengdu 611700 Sichuan Peoples R China Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England

linear support vector machine (LSVM) has proven to be an effective approach for link classification in sensor networks. In this paper, we present a data-driven framework for reliable link classification that models Kernelized linear support vector machine (KLSVM) to produce stable and consistent results. KLSVM is a linear classifying technique that learns the "best" parameter settings. We investigated its application to model and capture two phenomena: High dimensional multi-category classification and Spatiotemporal data correlation in wireless sensor network (WSN). In addition, the technique also detects anomalies within the network. With the optimized selection of the linear kernel hyperparameters, the technique models high-dimensional data classification and the examined packet traces exhibit correlations between link features. Link features with Packet Reception Rate (PRR) greater than 50% show a high degree of negative correlation while the other sensor node observations show a moderate degree of positive correlation. The model gives a good visual intuition of the network behavior. The efficiency of the supervised learning technique is studied over real dataset obtained from a WSN testbed. To achieve that, we examined packet traces from the 802.15.4 network. The technique has a good performance on link quality estimation accuracy and a precise anomaly detection of sensor nodes within the network.

关键词： linear support vector machine linear kernel correlation high dimensional multi-category data classification wireless sensor network

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Adaptable linear support vector machine

Adaptable Linear Support Vector Machine

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2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS 2015)

作者： Trung Le Van Nguyen Anh Nguyen Khanh Nguyen HCMc Univ Pedag Fac Informat Technol Ho Chi Minh City Vietnam

ISBN: (纸本)9781467366403

linear support vector machine (LSVM) has recently become one of the most prominent learning methods for solving classification and regression problems because of its applications in text classification, word-sense disambiguation, and drug design. However LSVM and its variations cannot adapt accordingly to a dynamic dataset nor learn in online mode. In this paper, we introduce an Adaptable linear support vector machine (ALSVM) which linearly scales with the size of training set. The most brilliant feature of ALSVM is that its decision boundary is adapted in a close form when adding or removing data.

关键词： linear support vector machine adaptable method linear method

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Convolution smoothing and online updating estimation for support vector machine

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TEST 2025年第1期34卷 288-323页

作者： Wang, Kangning Meng, Xiaoqing Sun, Xiaofei Shandong Technol & Business Univ Sch Stat Yantai Peoples R China

support vector machine (SVM) is a powerful binary classification statistical learning tool. In real applications, streaming data are common, which arrive in batches and have unbounded cumulative size. Because of the memory constraints of one single computer, the classical SVM solving the entire data together is unsuitable. Furthermore, the non-smoothness of hinge loss in SVM also poses high computational complexity. To overcome these issues, we first develop a convolution smoothing approach that achieves smooth and convex approximation to SVM. Then an online updating SVM is proposed, in which the estimators are renewed with current data and historical summary statistics. In theory, we prove that the convolution smoothing SVM achieves adequate approximation to SVM, and they are asymptotically equivalent in inference. Furthermore, the online updating SVM achieves the same efficiency as the classical SVM applying to the entire dataset. Numerical experiments on both synthetic and real data also validate our new methods.

关键词： linear support vector machine Convolution smoothing Bahadur representation Online updating algorithm

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support vector machine in big data: smoothing strategy and adaptive distributed inference

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STATISTICS AND COMPUTING 2024年第6期34卷 1-19页

作者： Wang, Kangning Liu, Jin Sun, Xiaofei Shandong Technol & Business Univ Sch Stat Yantai Peoples R China

support vector machine (SVM) is a powerful binary classification tool, but the growing size of modern data is bringing challenges to it. First, the non-smoothness of hinge loss poses difficulties in large-scale computation. Second, the existing large-scale distributed algorithms heavily rely on uniformity and randomness conditions, which are frequently violated in practice. To solve these issues, we first construct a convolution smoothing SVM, which enjoys a smooth and convex objective function. Then a distributed SVM is developed, in which the estimator can be calculated conveniently by minimizing a pilot sample-based distributed surrogate loss. In particular, it can be adaptive when the uniformity or randomness condition is violated. The established theoretical results and numerical experiments on both synthetic and real data all confirm the proposed methods.

关键词： linear support vector machine Convolution smoothing Bahadur representation Distributed algorithm

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A Data-Driven Classification Framework for Cybersecurity Breaches

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IT PROFESSIONAL 2024年第2期26卷 39-48页

作者： Rani, Priyanka Nag, Abhijit Kumar Shahriyar, Rifat Bangladesh Univ Engn & Technol Dhaka 1205 Bangladesh Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka 1205 Bangladesh Texas A&M Univ Cent Texas Subhani Dept Comp Informat Syst Killeen TX 76549 USA

Unauthorized access to sensitive or confidential data results in a data breach, which can cause significant harm to an organization. Reporting breaches and reviewing prior records can help reduce damages. To aid in preparation, antivirus and security companies have published data breach reports, but they can be difficult to comprehend and require substantial effort to study. This article proposes a data breach incident classification framework using machine learning algorithms (naive Bayes, logistic regression, support vector machine, and random forest) on a dataset from the Privacy Rights Clearinghouse. The framework's performance is evaluated using various metrics, including accuracy, F1 score, and confusion matrix. The article also employs topic modeling with latent Dirichlet allocation to enhance the classification's accuracy.

关键词： support vector machines Training Measurement machine Learning Algorithms Text Categorization Data Breach Feature Extraction Computer Security Classification Framework Logistic Regression machine Learning Learning Algorithms support vector machine Random Forest Performance Metrics F 1 Score Confusion Matrix Privacy Issues Random Forest Classifier Information Leakage Topic Modeling Text Classification Latent Dirichlet Allocation Deployment Model Software Companies Training Set Training Dataset Supervised Learning Term Frequency Inverse Document Frequency Synthetic Minority Oversampling Technique Performance Of Framework Train Test Split linear support vector machine Precision And Recall Minority Class Heavy Tailed Balanced Dataset Cloud Data

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Enhanced EEG-based cognitive workload detection using RADWT and machine learning

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NEUROSCIENCE 2025年 569卷 231-244页

作者： Ghasimi, Armin Shamekhi, Sina Sahand Univ Technol Fac Biomed Engn Tabriz Iran Sahand Univ Technol Biomed Engn Res Ctr Sahand New Town Tabriz Iran

Understanding cognitive workload improves learning performance and provides insights into human cognitive processes. Estimating cognitive workload finds practical applications in adaptive learning systems, braincomputer interfaces, and cognitive monitoring. In this work, different levels of cognitive workload are investigated, and a classification approach based on the Rational-Dilation Wavelet Transform (RADWT) is proposed. RADWT excels at capturing the oscillatory behavior of EEG signal sub-bands, offering high precision through its ability to adaptively analyze both temporal and spectral dynamics. Different classifications of machine learning and feature selection techniques were evaluated to get optimum classification accuracy and identify the most effective combination of features for the used dataset. The analysis shows that the most relevant brain region in differentiating cognitive workload levels is the frontal region, along with alpha and theta rhythm sub-bands. Integrating RADWT with a linear support vector machine (LSVM) and minimum Redundancy Maximum Relevance (mRMR) feature selection method yields notable classification accuracy. Concretely, the model yields accuracies of 96.6% for 0-back vs.3-back, 94.9% for 0-back vs 2-back, 92.3% for 2-back vs 3-back, and 81.7% for the three-class scenario. These results confirm the validity of the method proposed for estimating cognitive workload using the RADWT- and machine learning-based approach. The results also offer insights into neural mechanisms and a foundation for advanced applications in adaptive systems, brain-computer interfaces, and cognitive monitoring.

关键词： Cognitive Workload Electroencephalography Rational-Dilation Wavelet Transform minimum Redundancy Maximum Relevance linear support vector machine

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Very large scale integration implementation of seizure detection system with on-chip support vector machine classifier

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IET CIRCUITS DEVICES & SYSTEMS 2022年第1期16卷 1-12页

作者： Shanmugam, Shalini Dharmar, Selvathi Mepco Schlenk Engn Coll Dept Elect & Commun Engn Sivakasi Tamil Nadu India

Epilepsy is one of the most common neurological disorders;it affects millions of people globally. Because of the risks to health that it causes, the study and analysis of epilepsy have been given considerable attention in the biomedical field. In a neurological diagnosis, an automated device for detecting seizures or epilepsy from an electroencephalogram (EEG) signal has a significant role. This research work proposes a very large scale integration implementation system for the automatic detection of seizures. Before classification, feature extraction was performed by discrete wavelet transform (DWT) and on-chip classification was performed by a linear support vector machine. The polyphase architecture of Daubechies fourth-order wavelet three-level DWT was used to minimize computational time. The systolic array architecture-based support vector machine classifier using parallel processing helps to minimize the computational complexity of the proposed method. This research work uses an open access EEG dataset. Hardware implementation was done on a field-programmable gate array (FPGA). Efficient results were produced compared with the existing system on chip (SoC) and FPGA seizure detection systems.

关键词： on-chip classification medical signal processing neurophysiology epilepsy systolic arrays scale integration implementation system feature extraction automatic detection polyphase architecture hardware implementation signal classification medical disorders computational time common neurological disorders biomedical field linear support vector machine field-programmable gate array on-chip support vector machine classifier seizure detection system electroencephalogram signal neurological diagnosis medical signal detection support vector machines automated device field programmable gate arrays systolic array architecture-based support vector machine classifier Daubechies fourth-order wavelet three-level DWT electroencephalography wavelet transforms discrete wavelet transforms

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