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

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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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Knebel, Tilman, Sepp Hochreiter, and Klaus Obermayer. "An SMO Algorithm for the Potential Support Vector Machine." Neural Computation 20, no. 1 (2008): 271–87. http://dx.doi.org/10.1162/neco.2008.20.1.271.

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We describe a fast sequential minimal optimization (SMO) procedure for solving the dual optimization problem of the recently proposed potential support vector machine (P-SVM). The new SMO consists of a sequence of iteration steps in which the Lagrangian is optimized with respect to either one (single SMO) or two (dual SMO) of the Lagrange multipliers while keeping the other variables fixed. An efficient selection procedure for Lagrange multipliers is given, and two heuristics for improving the SMO procedure are described: block optimization and annealing of the regularization parameter ε. A co

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Jung, Kang-Mo. "Sparse Least Absolute Deviation Support Vector Machine." Korean Data Analysis Society 25, no. 5 (2023): 1701–12. http://dx.doi.org/10.37727/jkdas.2023.25.5.1701.

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The support vector machine solves a quadratic programming problem with linear inequality and equality constraints. However, it is not trivial to solve the quadratic problem. The least squares support vector machine(LS-SVM) solves a linear system by equality constraints instead of inequality constraints. LS-SVM is a popular method in regression and classification problems, because it effectively solves simple linear systems. There are two issues with the LS-SVM solution : the lack of robustness to outliers and the absence of sparseness. In this paper, we propose a sparse and robust support vect

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Peng, Lifang, Kefu Chen, Bin Huang, and Leyuan Zhou. "Breast Cancer Diagnosis Using an Ensemble Transfer Support Vector Machine." Journal of Medical Imaging and Health Informatics 11, no. 2 (2021): 332–36. http://dx.doi.org/10.1166/jmihi.2021.3260.

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As the number of breast cancer patients increases and the age of onset is younger, early detection and prevention have become the key to prevention and treatment of breast cancer. At present, many classification or clustering algorithms are used to diagnose breast cancer data. However, these algorithms directly lose the minimum source domain information, resulting in a significant improvement in the recognition rate. Based on this, this paper proposes an ensemble transfer support vector machine (ET-SVM) algorithm based on classic support vector machine (SVM). The algorithm can effectively use

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Adi, Jufriansah, Anggraini Ade, Zulfakriza, Khusnani Azmi, and Pramudya Yudhiakto. "Forecast earthquake precursor in the Flores Sea." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 3 (2023): 1825–36. https://doi.org/10.11591/ijeecs.v32.i3.pp1825-1836.

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Artificial intelligence (AI) can use seismic training data to discover relationships between inputs and outcomes in real-world applications. Despite this, particularly when using geographical data, it has not been used to predict earthquakes in the Flores Sea. The algorithm will read the seismic data as a pattern of iterations throughout the operation. The output data is created by grouping based on clusters using the most effective WCSS analysis, while the input features are derived from the original international resource information system (IRIS) web service data. Given that earthquake pred

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Zhao, Zhen Jiang, Wei Gao, Huai Zhong Wang, and Ke Fei Zhang. "Research in SVM Sample Optimizes of ISODATA Algorithm." Advanced Materials Research 532-533 (June 2012): 1507–11. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1507.

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Support Vector Machine is widely used in data classification, but in the case of more training samples, the training time is longer. To solve this problem, use the ISODATA clustering algorithm to cluster samples to obtain the new cluster center, together with high similarity to the error for the sample to form a new cluster of training samples, training support vector machines. So that a solution of high similarity to repeat the training samples of similar problems, while focusing on the easily lead to wrong classification of the training samples. The support vector machine classification accu

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Chen, Li Wei, and Chen Dong Wang. "Fault Diagnosis for Temperature Signal of Turbine Blade Based on LS-SVM." Applied Mechanics and Materials 385-386 (August 2013): 580–84. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.580.

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This document discusses the support vector machine (SVM) algorithm, then discusses least squares support vector machine (LS-SVM) algorithm, at the same time, the applications of SVM in the fault diagnosis of temperature signal of turbine blade being discussed, the least squares support vector machine algorithm being used in the research of fault diagnosis, being compared with LVQ neural network, experiments result show the operation speed of the least squares support vector machine algorithm is fast, its generalization ability is stronger, SVM can solve small sample learning problems as well a

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Sudharsan, V., and B. Yamuna. "Support Vector Machine based Decoding Algorithm for BCH Codes." Journal of Telecommunications and Information Technology, no. 2 (June 30, 2016): 108–12. http://dx.doi.org/10.26636/jtit.2016.2.728.

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Modern communication systems require robust, adaptable and high performance decoders for efficient data transmission. Support Vector Machine (SVM) is a margin based classification and regression technique. In this paper, decoding of Bose Chaudhuri Hocquenghem codes has been approached as a multi-class classification problem using SVM. In conventional decoding algorithms, the procedure for decoding is usually fixed irrespective of the SNR environment in which the transmission takes place, but SVM being a machinelearning algorithm is adaptable to the communication environment. Since the construc

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Stawska, Zofia. "SUPPORT VECTOR MACHINE IN GENDER RECOGNITION." Information System in Management 6, no. 4 (2017): 318–29. http://dx.doi.org/10.22630/isim.2017.6.4.6.

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In the paper, Support Vector Machine (SVM) methods are discussed. The SVM algorithm is a very strong classification tool. Its capability in gender recognition in comparison with the other methods is presented here. Different sets of face features derived from the frontal facial image such as eye corners, nostrils, mouth corners etc. are taken into account. The efficiency of different sets of facial features in gender recognition using SVM method is examined.

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Wahyudi, Diki, Muhammad Niswar, and A. Ais Prayogi Alimuddin. "WEBSITE PHISING DETECTION APPLICATION USING SUPPORT VECTOR MACHINE (SVM)." Journal of Information Technology and Its Utilization 5, no. 1 (2022): 18–24. http://dx.doi.org/10.56873/jitu.5.1.4836.

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Phishing is an act to get someone's important information in the form of usernames, passwords, and other sensitive information by providing fake websites that are similar to the original. Phishing (fishing for important information) is a form of criminal act that intends to obtain confidential information from someone, such as usernames, passwords and credit cards, by impersonating a trusted person or business in an official electronic communication, such as electronic mail or instant messages. Along with the development of the use of electronic media, which is followed by the increase in cybe

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Chen, Ziyi, Weiguo Zhao, Pingping Shen, Chengli Wang, and Yanfu Jiang. "A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM." Processes 12, no. 4 (2024): 809. http://dx.doi.org/10.3390/pr12040809.

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To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows and refining the sparrow position with the highest fitness value, we propose an enhanced sparrow search algorithm termed CLSSA. Subsequently, we optimize the parameters of support vector machines using this algorithm. A support vector machine classifier based on CLSSA has been constructed. Given the intrica

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GENOV, ROMAN, SHANTANU CHAKRABARTTY, and GERT CAUWENBERGHS. "SILICON SUPPORT VECTOR MACHINE WITH ON-LINE LEARNING." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 03 (2003): 385–404. http://dx.doi.org/10.1142/s0218001403002472.

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Training of support vector machines (SVMs) amounts to solving a quadratic programming problem over the training data. We present a simple on-line SVM training algorithm of complexity approximately linear in the number of training vectors, and linear in the number of support vectors. The algorithm implements an on-line variant of sequential minimum optimization (SMO) that avoids the need for adjusting select pairs of training coefficients by adjusting the bias term along with the coefficient of the currently presented training vector. The coefficient assignment is a function of the margin retur

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Wu, Wei. "Study of Improving Support Vector Machine Algorithm Based on Medical Data Mining." Advanced Materials Research 532-533 (June 2012): 1780–84. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1780.

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This paper introduces fundamental theory and mathematic model of Support Vector Machine(SVM), and also covers applying SVM algorithm in data assorting. In conventional SVM model, sample set always has noisy and isolated points, for solving this problem this paper proposes a SVM boundary sample cut algorithm: first, pre-select boundary samples, then apply Remove-Only algorithm to remove some inappropriate points, then the result will be final sample set for SVM. At last, we compared conventional and improved algorithms by applying them on categorizing two medical data sets; the accuracy of impr

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Kong, Rui, Qiong Wang, Gu Yu Hu, and Zhi Song Pan. "Fuzzy Asymmetric Support Vector Machines." Advanced Materials Research 433-440 (January 2012): 7479–86. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.7479.

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Support Vector Machines (SVM) has been extensively studied and has shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in medical diagnosis and detecting credit card fraud). In this paper, we propose the fuzzy asymmetric algorithm to augment SVMs to deal with imbalanced training-data problems, called FASVM, which is based on fuzzy memberships, combined with different error costs (DEC) algorithm. We compare the pe

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JU, Zhe, and Qingbao ZHANG. "Intuitionistic Fuzzy SVM based on Kernel Gray Relational Analysis." Proceedings of the Romanian Academy, Series A: Mathematics, Physics, Technical Sciences, Information Science 25, no. 4 (2024): 359–70. https://doi.org/10.59277/pra-ser.a.25.4.12.

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Fuzzy Support Vector Machine (FSVM) is a machine learning algorithm that combines fuzzy logic with Support Vector Machine (SVM) to deal with the uncertainty and fuzziness in classification and regression problems. This algorithm improves the performance of traditional SVM by introducing fuzzy membership degrees, making it more robust when handling datasets with noise or uncertainty. Although the existing FSVM algorithms can overcome the influence of noise to a certain extent, they cannot effectively distinguish outliers or abnormal values from boundary support vectors. To solve this problem, t

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Liu, Tianci, and Xiangjun Li. "Breast Cancer Prediction based on Support Vector Machine." International Journal of Biology and Life Sciences 3, no. 2 (2023): 4–6. http://dx.doi.org/10.54097/ijbls.v3i2.10061.

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Breast cancer is the most common malignant tumor among women in the world, and its mortality rate ranks second. In this paper, the breath cancer Wisconsin (diagnostic) data set is used for prediction. First, exploratory data analysis was carried out, and 10 indicators related to breast cancer were selected as independent variables. Then, support vector machine (SVM) and logistic regression model were used as classifiers, and 75% of data sets were divided into training sets to build models. Finally, 25% of test sets were used as input models for prediction, and accuracy and recall were used as

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Shi, Zhi Biao, Quan Gang Song, and Ming Zhao Ma. "Diagnosis for Vibration Fault of Steam Turbine Based on Modified Particle Swarm Optimization Support Vector Machine." Applied Mechanics and Materials 128-129 (October 2011): 113–16. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.113.

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Due to the influence of artificial factor and slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), this paper proposes a modified particle swarm optimization support vector machine (MPSO-SVM). A Steam turbine vibration fault diagnosis model was established and the failure data was used in fault diagnosis. The results of application show the model can get automatic optimization about the related parameters of support vector machine and achieve the ideal optimal solution globally. MPSO-SVM strategy is feasible and effective compared with

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Hadeel, Tariq Ibrahim, Jalil Mazher Wamidh, and Mahmood Jassim Enas. "Modified Harris Hawks optimizer for feature selection and support vector machine kernels." Modified Harris Hawks optimizer for feature selection and support vector machine kernels 29, no. 2 (2023): 942–53. https://doi.org/10.11591/ijeecs.v29.i2.pp942-953.

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The support vector machine (SVM), one of the most effective learning algorithms, has many real-world applications. The kernel type and its parameters have a significant impact on the SVM algorithm's effectiveness and performance. In machine learning, choosing the feature subset is a crucial step, especially when working with high-dimensional data sets. These crucial criteria were treated independently in the majority of earlier studies. In this research, we suggest a hybrid strategy based on the Harris Hawk optimization (HHO) algorithm. HHO is one of the lately suggested metaheuristic algo

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Xie, Tao, Jun Yao, and Zhiwei Zhou. "DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis." Processes 7, no. 5 (2019): 263. http://dx.doi.org/10.3390/pr7050263.

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As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis func

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Ebrahimabadi, Arash, and Alireza Afradi. "PERFORMANCE PREDICTION OF ROADHEADERS USING SUPPORT VECTOR MACHINE (SVM), FIREFLY ALGORITHM (FA) AND BAT ALGORITHM (BA)." Rudarsko-geološko-naftni zbornik 40, no. 3 (2025): 67–82. https://doi.org/10.17794/rgn.2025.3.6.

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Roadheaders play a crucial role in the excavation processes of tunnels and mines, offering efficient and precise cutting capabilities. The performance prediction of a roadheader is essential for optimizing operations and ensuring project success. By understanding the various factors that influence performance, implementing predictive models, and continuously improving machine design and operational strategies, the potential of roadheaders can be maximized. This article delves into the intricacies of performance prediction for roadheaders, exploring methods, case studies, challenges, and future

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Fletcher, Roger, and Gaetano Zanghirati. "Binary separation and training support vector machines." Acta Numerica 19 (May 2010): 121–58. http://dx.doi.org/10.1017/s0962492910000024.

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We introduce basic ideas of binary separation by a linear hyperplane, which is a technique exploited in the support vector machine (SVM) concept. This is a decision-making tool for pattern recognition and related problems. We describe a fundamental standard problem (SP) and show how this is used in most existing research to develop a dual-based algorithm for its solution. This algorithm is shown to be deficient in certain aspects, and we develop a new primal-based SQP-like algorithm, which has some interesting features. Most practical SVM problems are not adequately handled by a linear hyperpl

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He, Yan, Wei Zhang, Yongcai Ma, Jinyang Li, and Bo Ma. "The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM." Molecules 27, no. 13 (2022): 4091. http://dx.doi.org/10.3390/molecules27134091.

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Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectra

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Susanto, Anggita Dewi Novia Wardhani, and Hari Suparwito. "SVM-PSO Algorithm for Tweet Sentiment Analysis #BesokSenin." Indonesian Journal of Information Systems 6, no. 1 (2023): 36–47. http://dx.doi.org/10.24002/ijis.v6i1.7551.

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The hashtag #BesokSenin is a hashtag that is often trending on Indonesian Twitter on Sunday evenings. Many Indonesian Twitter users expressed their feelings about welcoming Monday using the hashtag #BesokSenin. The tweet containing #BesokSenin is known to be a motivational sentence to welcome Monday full of joy or a disappointed sentence because you have to return to your routine after taking a holiday on Saturday and Sunday. This study conducts sentiment analysis to find out the opinions of netizens on welcoming Mondays. The tweet data used is tweet data with the hashtag #BesokSenin and the k

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Tian, Li, Qiang Qiang Wang, and An Zhao Cao. "Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm." Applied Mechanics and Materials 291-294 (February 2013): 2164–68. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2164.

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With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

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Panayides, Michalis, and Andreas Artemiou. "Least Squares Minimum Class Variance Support Vector Machines." Computers 13, no. 2 (2024): 34. http://dx.doi.org/10.3390/computers13020034.

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In this paper, we propose a Support Vector Machine (SVM)-type algorithm, which is statistically faster among other common algorithms in the family of SVM algorithms. The new algorithm uses distributional information of each class and, therefore, combines the benefits of using the class variance in the optimization with the least squares approach, which gives an analytic solution to the minimization problem and, therefore, is computationally efficient. We demonstrate an important property of the algorithm which allows us to address the inversion of a singular matrix in the solution. We also dem

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Chen, Xiao Lin, Yan Jiang, Min Jie Chen, Yong Yu, Hong Ping Nie, and Min Li. "A Dynamic Cost Sensitive Support Vector Machine." Advanced Materials Research 424-425 (January 2012): 1342–46. http://dx.doi.org/10.4028/www.scientific.net/amr.424-425.1342.

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A lot of cost-sensitive support machine vector methods are used to handle the imbalanced datasets, but the obtained results are not as perfect as expectation. A promising method is proposed in this paper, named ADC-SVM, which uses genetic algorithm to dynamically search the optimal misclassification cost to build a cost sensitive support machine. We empirically evaluate ADC-SVM with SVM and Cost-sensitive SVM over 8 realistic imbalanced bi-class datasets from UCI. The experimental results show that ADC-SVM outperforms the other two methods over all the imbalanced datasets.

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TSAI, YIHJIA, and JIH PIN YEH. "SIMPLIFICATION OF SUPPORT VECTOR SOLUTIONS USING AN ARTIFICIAL BEE COLONY ALGORITHM." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 08 (2012): 1250020. http://dx.doi.org/10.1142/s0218001412500206.

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Support vector machines (SVMs) are a relatively recent machine learning technique. One of the SVM problems is that SVM is considerably slower in test phase caused by the large number of support vectors, which limits its practical use. To address this problem, we propose an artificial bee colony (ABC) algorithm to search for an optimal subset of the set of support vectors obtained through the training of the SVM, such that the original discriminant function is best approximated. Experimental results show that the proposed ABC algorithm outperforms some other compared methods in terms of the cla

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SHAKERIN, FARHAD, and GOPAL GUPTA. "White-box Induction From SVM Models: Explainable AI with Logic Programming." Theory and Practice of Logic Programming 20, no. 5 (2020): 656–70. http://dx.doi.org/10.1017/s1471068420000356.

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AbstractWe focus on the problem of inducing logic programs that explain models learned by the support vector machine (SVM) algorithm. The top-down sequential covering inductive logic programming (ILP) algorithms (e.g., FOIL) apply hill-climbing search using heuristics from information theory. A major issue with this class of algorithms is getting stuck in local optima. In our new approach, however, the data-dependent hill-climbing search is replaced with a model-dependent search where a globally optimal SVM model is trained first, then the algorithm looks into support vectors as the most influ

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Xiong, Jianbin, Qinghua Zhang, Qiong Liang, Hongbin Zhu, and Haiying Li. "Combining the Multi-Genetic Algorithm and Support Vector Machine for Fault Diagnosis of Bearings." Shock and Vibration 2018 (October 23, 2018): 1–13. http://dx.doi.org/10.1155/2018/3091618.

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Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection. In this paper, a parameter optimization algorithm for the SVM is proposed based on multi-genetic algorithm. The algorithm optimizes the correlation kernel parameters of the SVM using evolutionary search principles of multiple swarm genetic algorithms to obtain a superior SVM prediction model. The experimental results demonstrate that by combining the genetic algorithm and SVM algori

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Mishra, Akshansh, and Apoorv Vats. "Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints." Frattura ed Integrità Strutturale 15, no. 58 (2021): 242–53. http://dx.doi.org/10.3221/igf-esis.58.18.

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Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types i.e. regression algorithms and classification algorithms. In the present study, four supervised machine learning-based classification models i.e. Decision Trees algorithm, K- Nearest Neighbors (KNN) algorithm, Support Vector Machines (SVM) algorithm, and Ada Boost algorithm were subjected to the given dataset for the determination of fracture

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Wang, Lijun, Shengfei Ji, and Nanyang Ji. "Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults." Shock and Vibration 2018 (December 20, 2018): 1–13. http://dx.doi.org/10.1155/2018/8174860.

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This paper presents a method that combines Shuffled Frog Leaping Algorithm (SFLA) with Support Vector Machine (SVM) method in order to identify the fault types of rolling bearing in the gearbox. The proposed method improves the accuracy of fault diagnosis identification after processing the collected vibration signals through wavelet threshold denoising. The global optimization and high computational efficiency of SFLA are applied to the SVM model. Simulation results show that the SFLA-SVM algorithm is effective in fault diagnosis. Compared with SVM and Particle Swarm Optimization SVM (PSO-SVM

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Yin, Shen, Xin Gao, Hamid Reza Karimi, and Xiangping Zhu. "Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process." Abstract and Applied Analysis 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/836895.

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This paper investigates the proficiency of support vector machine (SVM) using datasets generated by Tennessee Eastman process simulation for fault detection. Due to its excellent performance in generalization, the classification performance of SVM is satisfactory. SVM algorithm combined with kernel function has the nonlinear attribute and can better handle the case where samples and attributes are massive. In addition, with forehand optimizing the parameters using the cross-validation technique, SVM can produce high accuracy in fault detection. Therefore, there is no need to deal with original

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Bhavsar, Hetal, and Amit Ganatra. "EuDiC SVM: A novel support vector machine classification algorithm." Intelligent Data Analysis 20, no. 6 (2016): 1285–305. http://dx.doi.org/10.3233/ida-150348.

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TSEHAY, ADMASSU ASSEGIE, S. J. SUSHMA, B.G BHAVYA, and S. PADMASHREE. "AN EFFICIENT SUPPORT VECTOR MACHINE BASED BREAST CANCER DETECTION MODEL." Seybold Report 16, no. 03 (2021): 79–86. https://doi.org/10.5281/zenodo.6553289.

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<strong>Abstract. </strong>In this research, a grid search approach is employed to develop an improved support vector machine (SVM) based breast cancer detection model. The grid search is used to find the best combinations of parameters that could maximize the accuracy of the SVM algorithm on breast cancer detection. Moreover, this study explores the effect of parameter tuning on the performance of SVM algorithm on breast cancer detection. The findings of this research shows that parameter tuning has a significant effect on the performance of the SVM algorithm on breast cancer detection. The e

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Liu, Xiao Hui, Yong Gang Xu, De Ying Guo, and Fei Liu. "Mill Gear Box of Intelligent Diagnosis Based on Support Vector Machine Parameters Optimization." Applied Mechanics and Materials 697 (November 2014): 239–43. http://dx.doi.org/10.4028/www.scientific.net/amm.697.239.

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For mill gearbox fault detection problems, and puts forward combining support vector machine (SVM) and genetic algorithm, is applied to rolling mill gear box fault intelligent diagnosis methods. The choice of parameters of support vector machine (SVM) is a very important for the SVM performance evaluation factors. For the selection of structural parameters of support vector machine (SVM) with no theoretical support, select and difficult cases, in order to reduce the SVM in this respect, puts forward the genetic algorithm to optimize parameters, and the algorithm of the model is applied to roll

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Sabrila, Trifebi Shina, Yufis Azhar, and Christian Sri Kusuma Aditya. "Analisis Sentimen Tweet Tentang UU Cipta Kerja Menggunakan Algoritma SVM Berbasis PSO." JISKA (Jurnal Informatika Sunan Kalijaga) 7, no. 1 (2022): 10–19. http://dx.doi.org/10.14421/jiska.2022.7.1.10-19.

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Support Vector Machine (SVM) is one of the most widely used classification algorithms for sentiment analysis and has been shown to provide satisfactory performance. However, despite its advantages, the SVM algorithm still has weaknesses in selecting the right SVM parameters to optimize the performance. In this study, sentiment analysis was done with the use of data called tweets about Undang-Undang Cipta Kerja which reap many pros and cons by the people in Indonesia, especially the laborers. The classification method used in this study is the Support Vector Machine algorithm which is optimized

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Mao, Guang Xiang. "Chaotic Ants Swarm Optimize Least Square Support Vector Machine." Applied Mechanics and Materials 644-650 (September 2014): 1564–68. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1564.

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This paper focuses on intelligent analysis of small samples in geotechnical engineering, the calculation process of SVM is simplified by applying LSSVM, the generalization performance of SVM is maintained utmost by using leave one out method, the approximation performance of SVM is optimized by applying wavelet kernel constructed from Marr wavelet, the parameters of SVM is optimized quickly and comprehensively through applying chaotic ants swarm algorithm, the convergence of chaotic ants swarm algorithm is accelerated by reconstituting part of ant-matrix in the process of optimization, and las

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Yue, Yan. "A Multi-Classified Method of Support Vector Machine (SVM) Based on Entropy." Applied Mechanics and Materials 241-244 (December 2012): 1629–32. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1629.

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Studies propose to combine standard SVM classification with the information entropy to increase SVM classification rate as well as reduce computational load of SVM testing. The algorithm uses the information entropy theory to per-treat samples’ attributes, and can eliminate some attributes which put small impacts on the date classification by introducing the reduction coefficient, and then reduce the amount of support vectors. The results show that this algorithm can reduce the amount of support vectors in the process of the classification with support vector machine, and heighten the recognit

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Andrianto, Fiki, Abdul Fadlil, and Imam Riadi. "Linear Kernel Optimization of Support Vector Machine Algorithm on Online Marketplace Sentiment Analysis." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 21, no. 1 (2024): 68–82. http://dx.doi.org/10.33751/komputasi.v21i1.9266.

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Twitter is a short message platform commonly used as a means of news information, commentary, and social interaction. One of the utilization of twitter is to analyze the sentiment of the online marketplace which can be used to determine the service, quality of goods, and delivery of goods on a product, service or application. This research aims to categorize the reviews or responses of the Indonesian people, especially to the online marketplace using the linear Support Vector Machine (SVM) algorithm. In order to make continuous improvements to the role of the Indonesian online marketplace in t

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Zhang, Yueqi, and Jiaming Chen. "Filter bank riemannian-based kernel support vector machine for motor imagery decoding." ITM Web of Conferences 47 (2022): 02013. http://dx.doi.org/10.1051/itmconf/20224702013.

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Brain computer interface (BCI) enables the communication between the brain and external machines through Electroencephalography (EEG) signals, which has attracted lots of attention. Motor Imagery-based BCI (MI-BCI) is one of the most important paradigms in the BCI field. In MI-BCI, machine learning algorithms can be employed for identifying the target limb of motor intention effectively. As a typical machine learning algorithm for motor imagery decoding, the Riemannian-based kernel support vector machine (RK-SVM) algorithm is not capable of feature extraction from multiple frequency bands, whi

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Widyarini, Liza, and Hindriyanto Dwi Purnomo. "Air Quality Prediction Using the Support Vector Machine Algorithm." Journal of Information Systems and Informatics 6, no. 2 (2024): 652–61. http://dx.doi.org/10.51519/journalisi.v6i2.705.

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Air quality is an important factor in maintaining the health and well-being of humans and the environment. To anticipate and manage air pollution, air quality prediction has become an important research topic. In this research, researchers propose using the Support Vector Machine (SVM) algorithm to predict air quality. SVM has proven to be an effective method in classification and regression, especially in the context of complex and non-linear data such as air quality data. Researchers utilized historical air quality datasets that include various parameters such as particulates, ozone, nitroge

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