Relevant bibliographies by topics / Support Vector Machine (SVM) algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Support Vector Machine (SVM) algorithm'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Support Vector Machine (SVM) algorithm"

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Cao, Jian, Shi Yu Sun, and Xiu Sheng Duan. "Optimal Boundary SVM Incremental Learning Algorithm." Applied Mechanics and Materials 347-350 (August 2013): 2957–62. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2957.

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Support vectors (SVs) cant be selected completely in support vector machine (SVM) incremental, resulting incremental learning process cant be sustained. In order to solve this problem, the article proposes optimal boundary SVM incremental learning algorithm. Based on in-depth analysis of the trend of the classification surface and make use of the KKT conditions, selecting the border of the vectors include the support vectors to participate SVM incremental learning. The experiment shows that the algorithm can be completely covered the support vectors and have the identical result with the class
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Li, Hong Mei, Lin Gen Yang, and Li Hua Zou. "The Research Based on GA-SVM Feature Selection Algorithm." Advanced Materials Research 532-533 (June 2012): 1497–502. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1497.

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To make feature subset which can gain the higher classification accuracy rate, the method based on genetic algorithms and the feature selection of support vector machine is proposed. Firstly, the ReliefF algorithm provides a priori information to GA, the parameters of the support vector machine mixed into the genetic encoding,and then using genetic algorithm finds the optimal feature subset and support vector machines parameter combination. Finally, experimental results show that the proposed algorithm can gain the higher classification accuracy rate based on the smaller feature subset.
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Yong-Hua Xu, Yong-Hua Xu. "Support Vector Machine based Automatic Classification Method for IoT big Data Features." 電腦學刊 34, no. 5 (2023): 015–27. http://dx.doi.org/10.53106/199115992023103405002.

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<p>As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then
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Xia, Xiao-Lei, Weidong Jiao, Kang Li, and George Irwin. "A Novel Sparse Least Squares Support Vector Machines." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/602341.

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The solution of a Least Squares Support Vector Machine (LS-SVM) suffers from the problem of nonsparseness. The Forward Least Squares Approximation (FLSA) is a greedy approximation algorithm with a least-squares loss function. This paper proposes a new Support Vector Machine for which the FLSA is the training algorithm—the Forward Least Squares Approximation SVM (FLSA-SVM). A major novelty of this new FLSA-SVM is that the number of support vectors is the regularization parameter for tuning the tradeoff between the generalization ability and the training cost. The FLSA-SVMs can also detect the l
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Nabat, Zahraa Modher, Mushtaq Talib Mahdi, and Shaymaa Abdul Hussein Shnain. "Face Recognition Method based on Support Vector Machine and Rain Optimization Algorithm (ROA)." Webology 19, no. 1 (2022): 2170–81. http://dx.doi.org/10.14704/web/v19i1/web19147.

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One basic study direction in pattern recognition research domain is Face recognition. Face recognition-based Authentication is used widely. Face recognition is related to non-linear issue; therefore, some techniques of artificial intelligence have been used in last few years to face recognition. According to recent results, support vector system classifiers (SVM) have excellent face recognition accuracy in pattern recognition in comparison with other classification methods. Although, support vector machine training parameters selection has great effect on the performance of support vector mach
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Liu, Yangwei, Hu Ding, Ziyun Huang, and Jinhui Xu. "Distributed and Robust Support Vector Machine." International Journal of Computational Geometry & Applications 30, no. 03n04 (2020): 213–33. http://dx.doi.org/10.1142/s0218195920500107.

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In this paper, we consider the distributed version of Support Vector Machine (SVM) under the coordinator model, where all input data (i.e., points in [Formula: see text] space) of SVM are arbitrarily distributed among [Formula: see text] nodes in some network with a coordinator which can communicate with all nodes. We investigate two variants of this problem, with and without outliers. For distributed SVM without outliers, we prove a lower bound on the communication complexity and give a distributed [Formula: see text]-approximation algorithm to reach this lower bound, where [Formula: see text
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Fujiwara, Shuhei, Akiko Takeda, and Takafumi Kanamori. "DC Algorithm for Extended Robust Support Vector Machine." Neural Computation 29, no. 5 (2017): 1406–38. http://dx.doi.org/10.1162/neco_a_00958.

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Nonconvex variants of support vector machines (SVMs) have been developed for various purposes. For example, robust SVMs attain robustness to outliers by using a nonconvex loss function, while extended [Formula: see text]-SVM (E[Formula: see text]-SVM) extends the range of the hyperparameter by introducing a nonconvex constraint. Here, we consider an extended robust support vector machine (ER-SVM), a robust variant of E[Formula: see text]-SVM. ER-SVM combines two types of nonconvexity from robust SVMs and E[Formula: see text]-SVM. Because of the two nonconvexities, the existing algorithm we pro
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Giustolisi, Orazio. "Using a multi-objective genetic algorithm for SVM construction." Journal of Hydroinformatics 8, no. 2 (2006): 125–39. http://dx.doi.org/10.2166/hydro.2006.016b.

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Support Vector Machines are kernel machines useful for classification and regression problems. In this paper, they are used for non-linear regression of environmental data. From a structural point of view, Support Vector Machines are particular Artificial Neural Networks and their training paradigm has some positive implications. In fact, the original training approach is useful to overcome the curse of dimensionality and too strict assumptions on statistics of the errors in data. Support Vector Machines and Radial Basis Function Regularised Networks are presented within a common structural fr
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Sameer, S. K. L., and P. Sriramya. "Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease." Alinteri Journal of Agriculture Sciences 36, no. 1 (2021): 713–20. http://dx.doi.org/10.47059/alinteri/v36i1/ajas21100.

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Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations
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Ovirianti, Nurul Huda, Muhammad Zarlis, and Herman Mawengkang. "Support Vector Machine Using A Classification Algorithm." SinkrOn 7, no. 3 (2022): 2103–7. http://dx.doi.org/10.33395/sinkron.v7i3.11597.

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Support vector machine is a part of machine learning approach based on statistical learning theory. Due to the higher accuracy of values, Support vector machines have become a focus for frequent machine learning users. This paper will introduce the basic theory of the Support vector machine, the basic idea of classification and the classification algorithm for the support vector machine that will be used. Solving the problem will use an algorithm, and prove the effectiveness of the algorithm on the data that has been used. In this study, the support vector machine has obtained very good accura
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Dissertations / Theses on the topic "Support Vector Machine (SVM) algorithm"

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Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Nella presente tesi di laurea viene preso in considerazione l’algoritmo di classificazione Support Vector Machine. Piu` in particolare si considera la sua formulazione come problema di ottimizazione Mixed Integer Program per la classificazione binaria super- visionata di un set di dati.
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Lau, Cidney. "Support Vector Machine Algorithm applied to Industrial Robot Error Recovery." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172331.

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A Machine Learning approach for error recovery in an industrial robot for the plastic mold industry isproposed in this master thesis project. The goal was to improve the present error recovery method byproviding a learning algorithm to the system instead of using the traditional algorithm-based control.The chosen method was the Support Vector Machine (SVM) due to the robustness and the goodgeneralization performance in real-world applications. Furthermore, SVM generates good classifierseven with a minimal number of training examples. In production, there will be no need for a humanoperator to
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Guan, Wei. "New support vector machine formulations and algorithms with application to biomedical data analysis." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41126.

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The Support Vector Machine (SVM) classifier seeks to find the separating hyperplane wx=r that maximizes the margin distance 1/||w||2^2. It can be formalized as an optimization problem that minimizes the hinge loss Ʃ[subscript i](1-y[subscript i] f(x[subscript i]))₊ plus the L₂-norm of the weight vector. SVM is now a mainstay method of machine learning. The goal of this dissertation work is to solve different biomedical data analysis problems efficiently using extensions of SVM, in which we augment the standard SVM formulation based on the application requirements. The biomedical applications
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Zhong, Wei. "Clustering System and Clustering Support Vector Machine for Local Protein Structure Prediction." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/7.

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Protein tertiary structure plays a very important role in determining its possible functional sites and chemical interactions with other related proteins. Experimental methods to determine protein structure are time consuming and expensive. As a result, the gap between protein sequence and its structure has widened substantially due to the high throughput sequencing techniques. Problems of experimental methods motivate us to develop the computational algorithms for protein structure prediction. In this work, the clustering system is used to predict local protein structure. At first, recurring
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Rogers, Spencer David. "Support Vector Machines for Classification and Imputation." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3215.

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Support vector machines (SVMs) are a powerful tool for classification problems. SVMs have only been developed in the last 20 years with the availability of cheap and abundant computing power. SVMs are a non-statistical approach and make no assumptions about the distribution of the data. Here support vector machines are applied to a classic data set from the machine learning literature and the out-of-sample misclassification rates are compared to other classification methods. Finally, an algorithm for using support vector machines to address the difficulty in imputing missing categorical data i
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Terrones, Michael. "A precise robotic arm positioning using an SVM classification algorithm." Diss., Online access via UMI:, 2007.

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Thesis (M.S.)--State University of New York at Binghamton, Department of Systems Science and Industrial Engineering, Thomas J. Watson School of Engineering and Applied Science, 2007.<br>Includes bibliographical references.
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Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.

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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chun
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Kroon, Rodney Stephen. "Support vector machines, generalization bounds, and transduction." Thesis, Stellenbosch : University of Stellenbosch, 2003. http://hdl.handle.net/10019.1/16375.

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Wang, Wenjuan. "Optimization algorithms for SVM classification : Applications to geometrical chromosome analysis." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30111/document.

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Le génome est très organisé au sein du noyau cellulaire. Cette organisation et plus spécifiquement la localisation et la dynamique des gènes et chromosomes contribuent à l'expression génétique et la différenciation des cellules que ce soit dans le cas de pathologies ou non. L'exploration de cette organisation pourrait dans le futur aider à diagnostiquer et identifier de nouvelles cibles thérapeutiques. La conformation des chromosomes peut être analysée grâce au marquage ADN sur plusieurs sites et aux mesures de distances entre ces différents marquages fluorescents. Dans ce contexte, l'organisa
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Chia, Yen Yee. "Integrating supercapacitors into a hybrid energy system to reduce overall costs using the genetic algorithm (GA) and support vector machine (SVM)." Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14394/.

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This research deals with optimising a supercapacitor-battery hybrid energy storage system (SB-HESS) to reduce the implementation cost for solar energy applications using the Genetic Algorithm (GA) and the Support Vector Machine (SVM). The integration of a supercapacitor into a battery energy storage system for solar applications is proven to prolong the battery lifespan. Furthermore, the reliability of the system was optimised using a GA within the Taguchi technique in the supercapacitor fabrication process. This is important to reduce the spread in tolerance of supercapacitors values (i.e. ca
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Books on the topic "Support Vector Machine (SVM) algorithm"

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Miao, Chuxiong, and Ming Zuo. A Support Vector Machine Model for Pipe Crack Size Classification: Reseach on SVM Classification. VDM Verlag Dr. Müller, 2010.

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Book chapters on the topic "Support Vector Machine (SVM) algorithm"

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Dong, Jian-xiong, Adam Krzyżak, and Ching Y. Suen. "A Fast SVM Training Algorithm." In Pattern Recognition with Support Vector Machines. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45665-1_5.

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Ye, Zijian, and Yi Mou. "Crayfish Quality Analysis Based on SVM and Infrared Spectra." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_99.

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AbstractDifferent algorithms combined with Near-infrared spectroscopy were investigated for the detection and classification of crayfish quality. In this study, the crawfish quality was predicted by partial least square-support vector machine, principal component analysis-support vector machine, BP neural network and support vector machine after pre-processing the NIR spectral data of crawfish. The result shows that the accuracy of near-infrared spectroscopy technology combined with SVM to classify crayfish quality can reach 100%, and the prediction can guide the sampling of crayfish food safety in practice, thus improving food safety and quality.
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Setiawan, Iwan, Evi Martaseli, Tugiman, et al. "Credit Risk Management Prediction Using the Support Vector Machine (SVM) Algorithm." In Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-084-8_18.

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Boughattas, Naouel, and Hanen Jabnoun. "Autism Spectrum Disorder (ASD) Detection Using Machine Learning Algorithms." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1_18.

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AbstractSome diseases are characterized by persistent deficits in brain activity. Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder. It appears in early childhood and evolves throughout life and needs to be detected early to accelerate the treatment and recovery process. These deficits may be detected using medical imaging techniques. In this paper, we present machine learning algorithms allowing to detect peoples with ASD from normal peoples. We used data from the ABIDE dataset. We tested 3 algorithms: Support Vector Machines (SVM), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). The best result was obtained using CNN algorithm with an accuracy equal to 95%.
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Cesaretti, Lorenzo, Laura Screpanti, David Scaradozzi, and Eleni Mangina. "Analysis of Educational Robotics Activities Using a Machine Learning Approach." In Makers at School, Educational Robotics and Innovative Learning Environments. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77040-2_27.

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AbstractThis paper presents the preliminary results of using machine learning techniques to analyze educational robotics activities. An experiment was conducted with 197 secondary school students in Italy: the authors updated Lego Mindstorms EV3 programming blocks to record log files with coding sequences students had designed in teams. The activities were part of a preliminary robotics exercise. We used four machine learning techniques—logistic regression, support-vector machine (SVM), K-nearest neighbors and random forests—to predict the students’ performance, comparing a supervised approach (using twelve indicators extracted from the log files as input for the algorithms) and a mixed approach (applying a k-means algorithm to calculate the machine learning features). The results showed that the mixed approach with SVM outperformed the other techniques, and that three predominant learning styles emerged from the data mining analysis.
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Anwar, Suzan, Arthur Rahming, Mikea Fernander, Otito Udedibor, and Shereen Ali. "Breast Cancer Diagnosing System: Using a Rough Set-Ensemble Classifier Approach." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88220-3_2.

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Abstract Breast cancer occurs when normal breast cells turn cancerous, grow abnormally and form tumors. The most common cancer impacting women worldwide is breast cancer. Diagnosing breast cancer early and accurately is crucial for giving the correct treatment and ensuring patients receive the best care possible. Due to human error, misdiagnosis is a possibility in the medical field. Over-diagnosis can cause patients to go through unnecessary treatments. Under-diagnosis can allow malignant tumors to become more aggressive and life-threatening. The aim of our research is to create a dependable model to correctly diagnose breast cancer. We propose to use a rough set ensemble classifier approach to assist doctors in making more accurate diagnosis. The rough set reduct algorithm will be used for feature reductions and the model will be built with logistic regression algorithm, Support Vector Machine (SVM) algorithms and random forest algorithm. The proposed model produced an accuracy of 93% for logistic regression algorithm, 97% for SVM, and 92% for Random Forest when classifying the image data and overall produced a 96% accuracy.
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Shawkat Ali, A. B. M. "Support Vector Machine." In Handbook of Research on Modern Systems Analysis and Design Technologies and Applications. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-887-1.ch028.

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From the beginning, machine learning methodology, which is the origin of artificial intelligence, has been rapidly spreading in the different research communities with successful outcomes. This chapter aims to introduce for system analysers and designers a comparatively new statistical supervised machine learning algorithm called support vector machine (SVM). We explain two useful areas of SVM, that is, classification and regression, with basic mathematical formulation and simple demonstration to make easy the understanding of SVM. Prospects and challenges of future research in this emerging area are also described. Future research of SVM will provide improved and quality access to the users. Therefore, developing an automated SVM system with state-of-the-art technologies is of paramount importance, and hence, this chapter will link up an important step in the system analysis and design perspective to this evolving research arena.
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Wang, Zhiyuan, Sayed Ameenuddin Irfan, Christopher Teoh, and Priyanka Hriday Bhoyar. "Support Vector Machine." In Numerical Machine Learning. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815136982123010008.

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In this chapter, we investigate Support Vector Machines (SVM) for both linearly separable and linearly non-separable cases, emphasizing accessibility by minimizing abstract mathematical theories. We present concrete numerical examples with small datasets and provide a step-by-step walkthrough, illustrating the inner workings of SVM. Additionally, we offer sample codes and comparisons with the SVM model available in the scikit-learn library. Upon completing this chapter, readers will gain a comprehensive understanding of SVM's mechanics, and its connection to the implementation and performance of the algorithm, and be well-prepared to apply it in their practical applications.
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Chopra, Deepti, and Roopal Khurana. "Support Vector Machine." In Introduction to Machine Learning with Python. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124422123010006.

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Support Vector Machine (SVM) may be defined as a machine learning algorithm that can be used for regression and classification. It is generally used for classification purposes. In this chapter, we will discuss Margin and Large Margin Methods and Kernel Methods.
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Ladwani, Vandana M. "Support Vector Machines and Applications." In Computer Vision. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch057.

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Support Vector Machines is one of the powerful Machine learning algorithms used for numerous applications. Support Vector Machines generate decision boundary between two classes which is characterized by special subset of the training data called as Support Vectors. The advantage of support vector machine over perceptron is that it generates a unique decision boundary with maximum margin. Kernalized version makes it very faster to learn as the data transformation is implicit. Object recognition using multiclass SVM is discussed in the chapter. The experiment uses histogram of visual words and multiclass SVM for image classification.
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Conference papers on the topic "Support Vector Machine (SVM) algorithm"

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Risa, Nafiatul, Didik Dwi Prasetya, Wahyu Nur Hidayat, Putrinda Inayatul Maula, I. Made Wirawan, and Satria Yuda Setiawan. "Sentiment Analysis of 'Kampus Merdeka' on Twitter Using Support Vector Machine (SVM) Algorithm." In 2024 IEEE 2nd International Conference on Electrical Engineering, Computer and Information Technology (ICEECIT). IEEE, 2024. https://doi.org/10.1109/iceecit63698.2024.10859978.

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Maulany, Gerzon Jokomen, Paulus Insap Santosa, and Indriana Hidayah. "Multiple Intelligence Learning Style Detection in E-learning using Support Vector Machine (SVM) Algorithm." In 2024 7th International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2024. http://dx.doi.org/10.1109/icicos62600.2024.10636915.

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Arivukarasi, M., A. Manju, R. Kaladevi, Shanmugasundaram Hariharan, M. Mahasree, and Andraju Bhanu Prasad. "Expression of Concern for: Efficient Phishing Detection and Prevention Using Support Vector Machine (SVM) Algorithm." In 2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2023. http://dx.doi.org/10.1109/csnt57126.2023.10703673.

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Punne, Mc Rore Rangga, Indrabayu, and Ingrid Nurtanio. "Mood Classification from Song Lyrics Using the Naive Bayes Algorithm, Support Vector Machine (SVM) and XGBoost." In 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). IEEE, 2024. http://dx.doi.org/10.1109/iaict62357.2024.10617452.

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Wu, Liyan, Yanlu Huang, Kai Jin, Shangya Han, Kun Xu, and Yanni Ou. "Enhanced Support Vector Machine Based Signal Recovery in Bandwidth-Limited 50-100 Gbit/s Flexible DS-PON." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.w1f.3.

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We proposed an adaptive signal recovery algorithm with reduced complexity based on the SVM principle for flexible downstream PON. Experimental results indicate a record-high link power budget of 24 dB for bandwidth-limited 100 Gbit/s direct-detection transmission@1E-3.
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Luaha, Lius, Fahmi Fahmi, and Muhammad Zarlis. "Performance Analysis of Support Vector Machine (SVM) Model Through Parameter Optimization with Genetic Algorithm (GA) in Chronic Kidney Disease Classification." In 2024 8th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM). IEEE, 2024. https://doi.org/10.1109/elticom64085.2024.10864385.

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Ram, Pancham, and Ritu Sibal. "A Novel Hybrid Approach to Enhance Software Quality by Using the Honey Badger Algorithm (HBA) and Support Vector Machine (SVM)." In 2025 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC). IEEE, 2025. https://doi.org/10.1109/iceccc65144.2025.11064181.

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Julianti, Dhea Syahira, Bayu Hananto, Ika Nurlaili Isnainiyah, Ridwan Raafi'udin, and Neny Rosmawarni. "Comparison of Support Vector Machine (SVM) and Random Forest Classifier Algorithms in the Classification of Acute Respiratory Infection (ARI) Disease." In 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). IEEE, 2024. https://doi.org/10.1109/icimcis63449.2024.10956291.

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Tamimi, Hammam, and Dirk Söffker. "Modeling of Flexible Structures by Means of Least Square Support Vector Machine." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9673.

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This paper investigates modeling of flexible structures by means of the least squares support vector machine (LS-SVM) algorithm. Modeling is the first step to obtain a suitable model-based controller for any given system. Accurate modeling of a flexible structure based on experimental data using LS-SVM algorithm requires less knowledge about the physical system. Least squares support vector machine algorithm can achieve global and unique solution when compared with other soft computing algorithms. Also, LS-SVM algorithm requires less training time. In this paper, the successful use of support
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Fan, XiaoJing, LaiBin Zhang, Wei Liang, and ZhaoHui Wang. "Leak Detection Method Based on Support Vector Machine." In 2008 7th International Pipeline Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/ipc2008-64118.

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Assumptive and uncertain factors, few leak samples, complex non-linear pipeline systems are the problems often involved in the process of pipeline leak detection. Furthermore, the pressure wave changes of leakage are similar to these of valve regulation and pump closure. Thus it is difficult to establish a reliable model and to distinguish the leak signal pattern from others in pipeline leak detection. The veracity of leak detection system is limited. This paper presents a novel technique based on the statistical learning theory, support vector machine (SVM) for pipeline leak detection. Suppor
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Reports on the topic "Support Vector Machine (SVM) algorithm"

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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O'Neill, Francis, Kristofer Lasko, and Elena Sava. Snow-covered region improvements to a support vector machine-based semi-automated land cover mapping decision support tool. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45842.

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Abstract:
This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overa
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