Relevant bibliographies by topics / Support vetor machine (SVM)

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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 vetor machine (SVM)'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Support vetor machine (SVM)"

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BHAVANI, PADALA SAI, and PEETHALA BINDHU PRIYA. "SVM Powered Flower Species Classification." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04843.

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The Iris flower classification problem is a classic example of multi-class classification used to demonstrate the effectiveness of machine learning algorithms. This project applies supervised learning techniques to accurately predict the species of an Iris flower based on four key features: sepal length, sepal width, petal length, and petal width. Using the well-known Iris dataset, we implemented and evaluated several machine learning models, including Logistic Regression, k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), and Decision Trees. The dataset was preprocessed and split into
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Tommy Rustandi, Sekhan Rozaki Kusuma Wardana, Didi Suhaedi, and Yurika Pemanasari. "Pemetaan Hyperplane Pada Support Vector Machine." Bandung Conference Series: Mathematics 3, no. 2 (2023): 109–19. http://dx.doi.org/10.29313/bcsm.v3i2.8187.

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Abstrak. Penelitian ini difokuskan pada klasifikasi biner dengan data linear, dengan tujuan untuk memahami penggunaan pemetaan hyperplane dalam klasifikasi data menggunakan SVM dan bagaimana contoh penerapannya dalam dunia nyata. Metode penelitian yang digunakan meliputi studi literatur terhadap contoh-contoh penerapan SVM dengan pemetaan hyperplane. Hasil penelitian menunjukkan bahwa pemetaan hyperplane penting dalam klasifikasi data dengan SVM. Pemetaan ini memungkinkan SVM untuk memisahkan dua kelas dengan optimal dalam ruang fitur yang lebih tinggi, sehingga meningkatkan performa klasifika
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Tian, Yingjie, Yong Shi, and Xiaohui Liu. "RECENT ADVANCES ON SUPPORT VECTOR MACHINES RESEARCH." Technological and Economic Development of Economy 18, no. 1 (2012): 5–33. http://dx.doi.org/10.3846/20294913.2012.661205.

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Support vector machines (SVMs), with their roots in Statistical Learning Theory (SLT) and optimization methods, have become powerful tools for problem solution in machine learning. SVMs reduce most machine learning problems to optimization problems and optimization lies at the heart of SVMs. Lots of SVM algorithms involve solving not only convex problems, such as linear programming, quadratic programming, second order cone programming, semi-definite programming, but also non-convex and more general optimization problems, such as integer programming, semi-infinite programming, bi-level programm
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Rizal, Reyhan Achmad, Imron Sanjaya Girsang, and Sidik Apriyadi Prasetiyo. "Klasifikasi Wajah Menggunakan Support Vector Machine (SVM)." REMIK (Riset dan E-Jurnal Manajemen Informatika Komputer) 3, no. 2 (2019): 1. http://dx.doi.org/10.33395/remik.v3i2.10080.

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Klasifikasi wajah merupakan teknik yang dapat digunakan untuk membedakan karakteristik pola wajah seseorang. Sistem klasifikasi wajah adalah suatu aplikasi yang membuat sebuah mesin dapat mengenali wajah seseorang sesuai dengan citra wajah yang telah ditraining dan disimpan di dalam database mesin tersebut. Klasifikasi wajah sendiri dapat dilakukan dengan berbagai cara, salah satunya adalah menggunakan metode support vector machine (SVM). Penelitian ini dilakukan dengan sampling yang di ambil dalam variasi posisi pada sudut kemiringan subjek (-90°, -70°, -45°, -25°, -5° ) dan (+90°, +70°, +45°
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Kurita, Takio. "Support Vector Machine and Generalization." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (2004): 84–92. http://dx.doi.org/10.20965/jaciii.2004.p0084.

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The support vector machine (SVM) has been extended to build up nonlinear classifiers using the kernel trick. As a learning model, it has the best recognition performance among the many methods currently known because it is devised to obtain high performance for unlearned data. This paper reviews how to enhance generalization in learning classifiers centering on the SVM.
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Pan, Yuqing, Wenpeng Zhai, Wei Gao, and Xiangjun Shen. "If-SVM: Iterative factoring support vector machine." Multimedia Tools and Applications 79, no. 35-36 (2020): 25441–61. http://dx.doi.org/10.1007/s11042-020-09179-9.

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Panja, Rupan, and Nikhil R. Pal. "MS-SVM: Minimally Spanned Support Vector Machine." Applied Soft Computing 64 (March 2018): 356–65. http://dx.doi.org/10.1016/j.asoc.2017.12.017.

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Abe, Shigeo. "Minimal Complexity Support Vector Machines for Pattern Classification." Computers 9, no. 4 (2020): 88. http://dx.doi.org/10.3390/computers9040088.

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Minimal complexity machines (MCMs) minimize the VC (Vapnik-Chervonenkis) dimension to obtain high generalization abilities. However, because the regularization term is not included in the objective function, the solution is not unique. In this paper, to solve this problem, we discuss fusing the MCM and the standard support vector machine (L1 SVM). This is realized by minimizing the maximum margin in the L1 SVM. We call the machine Minimum complexity L1 SVM (ML1 SVM). The associated dual problem has twice the number of dual variables and the ML1 SVM is trained by alternatingly optimizing the du
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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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Cuong, Nguyen The, and Huynh The Phung. "WEIGHTED STRUCTURAL SUPPORT VECTOR MACHINE." Journal of Computer Science and Cybernetics 37, no. 1 (2021): 43–56. http://dx.doi.org/10.15625/1813-9663/37/1/15396.

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In binary classification problems, two classes of data seem to be different from each other. It is expected to be more complicated due to the clusters in each class also tend to be different. Traditional algorithms as Support Vector Machine (SVM) or Twin Support Vector Machine (TWSVM) cannot sufficiently exploit structural information with cluster granularity of the data, cause limitation on the capability of simulation of data trends. Structural Twin Support Vector Machine (S-TWSVM) sufficiently exploits structural information with cluster granularity for learning a represented hyperplane. Th
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Dissertations / Theses on the topic "Support vetor machine (SVM)"

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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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Armond, Kenneth C. Jr. "Distributed Support Vector Machine Learning." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/711.

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Support Vector Machines (SVMs) are used for a growing number of applications. A fundamental constraint on SVM learning is the management of the training set. This is because the order of computations goes as the square of the size of the training set. Typically, training sets of 1000 (500 positives and 500 negatives, for example) can be managed on a PC without hard-drive thrashing. Training sets of 10,000 however, simply cannot be managed with PC-based resources. For this reason most SVM implementations must contend with some kind of chunking process to train parts of the data at a time (10 ch
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Dhakal, Santosh. "Concrete Strength Prediction Modeling based on Support Vector Machine (SVM)." OpenSIUC, 2015. https://opensiuc.lib.siu.edu/theses/1802.

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Strength of concrete is the major parameter in the design of structures and is represented by the 28-day compressive strength of concrete. Many earlier studies proved that the compressive strength of concrete is not only related to w/c ratio but also rely on proportion of other constituent materials. Application of recently developed new generation admixtures for the production of high performance concrete, has made the concrete strength prediction complex and highly nonlinear challenging the research engineers and data scientists. Development of early accurate prediction model for concrete st
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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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Shantilal. "SUPPORT VECTOR MACHINE FOR HIGH THROUGHPUT RODENT SLEEP BEHAVIOR CLASSIFICATION." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_theses/506.

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This thesis examines the application of a Support Vector Machine (SVM) classifier to automatically detect sleep and quiet wake (rest) behavior in mice from pressure signals on their cage floor. Previous work employed Neural Networks (NN) and Linear Discriminant Analysis (LDA) to successfully detect sleep and wake behaviors in mice. Although the LDA was successful in distinguishing between the sleep and wake behaviors, it has several limitations, which include the need to select a threshold and difficulty separating additional behaviors with subtle differences, such as sleep and rest. The SVM h
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Westlinder, Simon. "Video Traffic Classification : A Machine Learning approach with Packet Based Features using Support Vector Machine." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-43011.

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Internet traffic classification is an important field which several stakeholders are dependent on for a number of different reasons. Internet Service Providers (ISPs) and network operators benefit from knowing what type of traffic that propagates over their network in order to correctly treat different applications. Today Deep Packet Inspection (DPI) and port based classification are two of the more commonly used methods in order to classify Internet traffic. However, both of these techniques fail when the traffic is encrypted. This study explores a third method, classifying Internet traffic b
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Zarogianni, Eleni. "Machine learning and brain imaging in psychosis." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/22814.

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Over the past years early detection and intervention in schizophrenia have become a major objective in psychiatry. Early intervention strategies are intended to identify and treat psychosis prior to fulfilling diagnostic criteria for the disorder. To this aim, reliable early diagnostic biomarkers are needed in order to identify a high-risk state for psychosis and also predict transition to frank psychosis in those high-risk individuals destined to develop the disorder. Recently, machine learning methods have been successfully applied in the diagnostic classification of schizophrenia and in pre
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Tarasova, Natalya. "Classification of Hate Tweets and Their Reasons using SVM." Thesis, Uppsala universitet, Avdelningen för datalogi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-275782.

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Denna studie fokuserar på att klassificera hat-meddelanden riktade mot mobiloperatörerna Verizon,  AT&T and Sprint. Huvudsyftet är att med hjälp av maskininlärningsalgoritmen Support Vector Machines (SVM) klassificera meddelanden i fyra kategorier - Hat, Orsak, Explicit och Övrigt - för att kunna identifiera ett hat-meddelande och dess orsak. Studien resulterade i två metoder: en "naiv" metod (the Naive Method, NM) och en mer "avancerad" metod (the Partial Timeline Method, PTM). NM är en binär metod i den bemärkelsen att den ställer frågan: "Tillhör denna tweet klassen Hat?". PTM ställer s
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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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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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Books on the topic "Support vetor machine (SVM)"

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-W, Lee S., and Verri Alessandro, eds. Pattern recognition with support vector machines: First international workshop, SVM 2002, Niagara Falls, Canada, August 202 : proceedings. Springer, 2002.

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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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(Editor), Seong-Whan Lee, and Alessandro Verri (Editor), eds. Pattern Recognition with Support Vector Machines: First International Workshop, SVM 2002, Niagara Falls, Canada, August 10, 2002. Proceedings (Lecture Notes in Computer Science). Springer, 2002.

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Lee, Seong-Whan, and Alessandro Verri. Pattern Recognition with Support Vector Machines: First International Workshop, SVM 2002, Niagara Falls, Canada, August 10, 2002. Proceedings. Springer London, Limited, 2003.

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Book chapters on the topic "Support vetor machine (SVM)"

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Madhavan, Govindakumar. "Support Vector Machine (SVM)." In Transactions on Computer Systems and Networks. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9914-5_14.

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Vasudevan, Shriram K., Nitin Vamsi Dantu, Sini Raj Pulari, and T. S. Murugesh. "Support Vector Machines (SVM)." In Machine Learning with oneAPI. CRC Press, 2023. http://dx.doi.org/10.1201/9781003393122-7.

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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Support Vector Machines and Support Vector Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_9.

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AbstractIn this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation of the svm along with some kernel functions that are fundamental for building the different kernels methods that are allowed in svm. We explain how the svm for binary response variables can be expanded for categorical response variables and give
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Yadav, Nishu, Astha Singh, and Divya Kumar. "Video-Based Depression Detection Using Support Vector Machine (SVM)." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10766-5_25.

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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 safe
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Rud, Samuel, and Jiann-Shiou Yang. "A Support Vector Machine (SVM) Classification Approach to Heart Murmur Detection." In Advances in Neural Networks - ISNN 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13318-3_7.

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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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Yoon, Yong. "Spatial Choice Modeling Using the Support Vector Machine (SVM): Characterization and Prediction." In Predictive Econometrics and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70942-0_55.

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Khamis, Siti Aqilah, Cik Feresa Mohd Foozy, Mohd Firdaus Ab Aziz, and Nordiana Rahim. "Header Based Email Spam Detection Framework Using Support Vector Machine (SVM) Technique." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36056-6_6.

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Femina Jalin, A., and J. Jayakumari. "A Robust Tamil Text to Speech Synthesizer Using Support Vector Machine (SVM)." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3992-3_68.

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Conference papers on the topic "Support vetor machine (SVM)"

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Jayapriya, P., Kishore Vel I V, Kishore P, Logesh Krishna M R, and Naveen Raja S. "Hate Speech Detection Using Support Vector Machine (SVM)." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716982.

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Qasim, Osama Mohammed, and Nada Abdullah Rasheed. "Web Scraping Social Media Using Support Vector Machine (SVM)." In 2024 3rd International Conference on Advances in Engineering Science and Technology (AEST). IEEE, 2024. https://doi.org/10.1109/aest63017.2024.10960239.

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Krisdianto, Ricky, Ivana Apriani, Hossey Masada, and Hidayaturrahman. "Performance Analysis of Support Vector Machine (SVM) for Diabetes Disease Detection." In 2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2024. http://dx.doi.org/10.1109/aidas63860.2024.10730403.

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Misra, Praveen Kumar, Rahul Gupta, and Barkha Gupta. "Diabetes Prediction using Logistic Regression and Support Vector Machine (SVM) Classifier." In 2024 International Conference on Signal Processing and Advance Research in Computing (SPARC). IEEE, 2024. https://doi.org/10.1109/sparc61891.2024.10829296.

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Rambharat, Yadav Abhishek, Dev Ritesh Shelke, Kundlik Adnan Khaleel, Anande Aditya Arunkumar, and Deepa Ekhande. "SmartGuard: Support Vector Machine(SVM)-Powered Defense Mechanism for Phishing Prevention." In 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). IEEE, 2024. https://doi.org/10.1109/icaiqsa64000.2024.10882417.

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Kaur, Arpanpreet, Kanwarpartap Singh Gill, Deepak Upadhyay, and Swati Devliyal. "Safeguarding Hearts using Support Vector Machine (SVM) Analysis for Early Heart Disease Prediction." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10838031.

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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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Mahfouz, Tarek, and Amr Kandil. "Construction Legal Decision Support Using Support Vector Machine (SVM)." In Construction Research Congress 2010. American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41109(373)88.

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Md.Ripon, Abu Hanif, and Muhammad Ahsan Ullah. "Rotating Machine Fault Detection Using Support Vector Machine (SVM) Classifier." In 2023 4th International Conference on Computing and Communication Systems (I3CS). IEEE, 2023. http://dx.doi.org/10.1109/i3cs58314.2023.10127320.

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Yang, Zhixiong, and Waheed U. Bajwa. "RD-SVM: A resilient distributed support vector machine." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472116.

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Reports on the topic "Support vetor machine (SVM)"

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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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Karlsson, Hyunjoo Kim, and Yushu Li. Investigation of Swedish krona exchange rate volatilityby APARCH-Support Vector Regression. Department of Economics and Statistics, Linnaeus University, 2024. http://dx.doi.org/10.15626/ns.wp.2024.10.

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This paper investigates daily exchange rate volatility behaviors with a focus on a small open economy’s currency, the Swedish krona (SEK), against four currencies: the U.S. dollar, Euro, the Pound Sterling (GBP), and the Norwegian krone (NOK) over the whole period from Jan. 2010 to March 2023, whereas the whole period is divided into different sub-sample periods based on the economic events. In the framework of APARCH models, we find that volatility behavior of the Swedish krona (SEK) exchange rates varies across different currency pairs (SEK being included in all cases) and sub-sample periods
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Emma, Olsson. Kolinlagring med biokol : Att nyttja biokol och hydrokol som kolsänka i östra Mellansverige. Linköping University Electronic Press, 2025. https://doi.org/10.3384/9789180759496.

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Pest inventory of a field is a way of knowing when the thresholds for pest control is reached. It is of increasing interest to use machine learning to automate this process, however, many challenges arise with detection of small insects both in traps and on plants. This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machine were implemented. Trap detection with neural network models and a check size function were test
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