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Relevant bibliographies by topics / SVM-SMO

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Academic literature on the topic 'SVM-SMO'

Author: Grafiati

Published: 3 June 2025

Last updated: 6 July 2025

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Journal articles on the topic "SVM-SMO"

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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 comparison of the variants shows that the dual SMO, including block optimization and annealing, performs efficiently in terms of computation time. In contrast to standard support vector machines (SVMs), the P-SVM is applicable to arbitrary dyadic data sets, but benchmarks are provided against libSVM's ε-SVR and C-SVC implementations for problems that are also solvable by standard SVM methods. For those problems, computation time of the P-SVM is comparable to or somewhat higher than the standard SVM. The number of support vectors found by the P-SVM is usually much smaller for the same generalization performance.

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Torres-Barrán, Alberto, Carlos M. Alaíz, and José R. Dorronsoro. "Faster SVM training via conjugate SMO." Pattern Recognition 111 (March 2021): 107644. http://dx.doi.org/10.1016/j.patcog.2020.107644.

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Keerthi, S. S., and S. K. Shevade. "SMO Algorithm for Least-Squares SVM Formulations." Neural Computation 15, no. 2 (2003): 487–507. http://dx.doi.org/10.1162/089976603762553013.

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This article extends the well-known SMO algorithm of support vector machines (SVMs) to least-squares SVM formulations that include LS-SVM classification, kernel ridge regression, and a particular form of regularized kernel Fisher discriminant. The algorithm is shown to be asymptotically convergent. It is also extremely easy to implement. Computational experiments show that the algorithm is fast and scales efficiently (quadratically) as a function of the number of examples.

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Keerthi, S. S., S. K. Shevade, C. Bhattacharyya, and K. R. K. Murthy. "Improvements to Platt's SMO Algorithm for SVM Classifier Design." Neural Computation 13, no. 3 (2001): 637–49. http://dx.doi.org/10.1162/089976601300014493.

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This article points out an important source of inefficiency in Platt's sequential minimal optimization (SMO) algorithm that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO. These modified algorithms perform significantly faster than the original SMO on all benchmark data sets tried.

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Shevade, S. K., S. S. Keerthi, C. Bhattacharyya, and K. R. K. Murthy. "Improvements to the SMO algorithm for SVM regression." IEEE Transactions on Neural Networks 11, no. 5 (2000): 1188–93. http://dx.doi.org/10.1109/72.870050.

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Feng Cai and V. Cherkassky. "Generalized SMO Algorithm for SVM-Based Multitask Learning." IEEE Transactions on Neural Networks and Learning Systems 23, no. 6 (2012): 997–1003. http://dx.doi.org/10.1109/tnnls.2012.2187307.

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Gomathy, V., and Dr S. Sumathi. "IMPLEMENTATION OF SVM USING SEQUENTIAL MINIMAL OPTIMIZATION FOR POWER TRANSFORMER FAULT ANALYSIS USING DGA." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 5 (2013): 1687–99. http://dx.doi.org/10.24297/ijct.v10i5.4153.

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Reliable operations of power transformers are necessary for effective transmission and distribution of power supply. During normal functions of the power transformer, distinct types of faults occurs due to insulation failure, oil aging products, overheating of windings, etc., affect the continuity of power supply thus leading to serious economic losses. To avoid interruptions in the power supply, various software fault diagnosis approaches are developed to detect faults in the power transformer and eliminate the impacts. SVM and SVM-SMO are the software fault diagnostic techniques developed in this paper for the continuous monitoring and analysis of faults in the power transformer. The SVM algorithm is faster, conceptually simple and easy to implement with better scaling properties for few training samples. The performances of SVM for large training samples are complex, subtle and difficult to implement. In order to obtain better fault diagnosis of large training data, SVM is optimized with SMO technique to achieve high interpretation accuracy in fault analysis of power transformer. The proposed methods use Dissolved Gas-in-oil Analysis (DGA) data set obtained from 500 KV main transformers of Pingguo Substation in South China Electric Power Company. DGA is an important tool for diagnosis and detection of incipient faults in the power transformers. The Gas Chromatograph (GC) is one of the traditional methods of DGA, utilized to choose the most appropriate gas signatures dissolved in transformer oil to detect types of faults in the transformer. The simulations are carried out in MATLAB software with an Intel core 3 processor with speed of 3 GHZ and 2 GB RAM PC. The results obtained by optimized SVM and SVM-SMO are compared with the existing SVM classification techniques. The test results indicate that the SVM-SMO approach significantly improve the classification accuracy and computational time for power transformer fault classification.

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Tian, Li Yan, and Xiao Guang Hu. "Method of Parallel Sequential Minimal Optimization for Fast Training Support Vector Machine." Applied Mechanics and Materials 29-32 (August 2010): 947–51. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.947.

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A fast training support vector machine using parallel sequential minimal optimization is presented in this paper. Up to now, sequential minimal optimization (SMO) is one of the major algorithms for training SVM, but it still requires a large amount of computation time for the large sample problems. Unlike the traditional SMO, the parallel SMO partitions the entire training data set into small subsets first and then runs multiple CPU processors to seal with each of the partitioned data set. Experiments show that the new algorithm has great advantage in terms of speediness when applied to problems with large training sets and high dimensional spaces without reducing generalization performance of SVM.

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Glasmachers, Tobias, and Christian Igel. "Second-Order SMO Improves SVM Online and Active Learning." Neural Computation 20, no. 2 (2008): 374–82. http://dx.doi.org/10.1162/neco.2007.10-06-354.

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Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow online and active learning. Second, for large data sets, computing the exact SVM solution may be too time-consuming, and an efficient approximation can be preferable. The powerful LASVM iteratively approaches the exact SVM solution using sequential minimal optimization (SMO). It allows efficient online and active learning. Here, this algorithm is considerably improved in speed and accuracy by replacing the working set selection in the SMO steps. A second-order working set selection strategy, which greedily aims at maximizing the progress in each single step, is incorporated.

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Ma, Xin, Jiansheng Wu, and Xiaoyun Xue. "Identification of DNA-Binding Proteins Using Support Vector Machine with Sequence Information." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/524502.

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DNA-binding proteins are fundamentally important in understanding cellular processes. Thus, the identification of DNA-binding proteins has the particularly important practical application in various fields, such as drug design. We have proposed a novel approach method for predicting DNA-binding proteins using only sequence information. The prediction model developed in this study is constructed by support vector machine-sequential minimal optimization (SVM-SMO) algorithm in conjunction with a hybrid feature. The hybrid feature is incorporating evolutionary information feature, physicochemical property feature, and two novel attributes. These two attributes use DNA-binding residues and nonbinding residues in a query protein to obtain DNA-binding propensity and nonbinding propensity. The results demonstrate that our SVM-SMO model achieves 0.67 Matthew's correlation coefficient (MCC) and 89.6% overall accuracy with 88.4% sensitivity and 90.8% specificity, respectively. Performance comparisons on various features indicate that two novel attributes contribute to the performance improvement. In addition, our SVM-SMO model achieves the best performance than state-of-the-art methods on independent test dataset.

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Dissertations / Theses on the topic "SVM-SMO"

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Bonansea, Lucas. "3D Hand gesture recognition using a ZCam and an SVM-SMO classifier." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1468148.

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Noronha, Daniel Holanda. "Proposta de implementa??o em FPGA de m?quina de vetores de suporte (SVM) utilizando otimiza??o sequencial m?nima (SMO)." PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA EL?TRICA E DE COMPUTA??O, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/24416.

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Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-12-01T23:34:00Z No. of bitstreams: 1 DanielHolandaNoronha_DISSERT.pdf: 2617561 bytes, checksum: 88cfc246d074eabfd971d5b81edbf109 (MD5)<br>Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-12-05T21:07:17Z (GMT) No. of bitstreams: 1 DanielHolandaNoronha_DISSERT.pdf: 2617561 bytes, checksum: 88cfc246d074eabfd971d5b81edbf109 (MD5)<br>Made available in DSpace on 2017-12-05T21:07:18Z (GMT). No. of bitstreams: 1 DanielHolandaNoronha_DISSERT.pdf: 2617561 bytes, checksum: 88cfc246d074eabfd971d5b81edbf109 (MD5) Previous issue date: 2017-11-20<br>A import?ncia do uso de FPGAs como aceleradores vem crescendo fortemente nos ?ltimos anos. Companhias como Amazon e Microsoft est?o incorporando FPGAs em seus data centers, objetivando especialmente acelerar algoritmos em suas ferramentas de busca. No centro dessas aplica??es est?o algoritmos de aprendizado de m?quina, como ? o caso da M?quina de Vetor de Suporte (SVM). Entretanto, para que essas aplica??es obtenham a acelera??o desejada, o uso eficiente dos recursos das FPGAs ? necess?rio. O projeto possui como objetivo a implementa??o paralela em hardware tanto da fase feed-forward de uma M?quina de Vetores de Suporte (SVM) quanto de sua fase de treinamento. A fase feed-forward (infer?ncia) ? implementada utilizando o kernel polinomial e de maneira totalmente paralela, visando obter a m?xima acelera??o poss?vel ao custo de uma maior utiliza??o da ?rea dispon?vel. Al?m disso, a implementa??o proposta para a infer?ncia ? capaz de computar tanto a classifica??o quanto a regress?o utilizando o mesmo hardware. J? o treinamento ? feito utilizando Otimiza??o Sequencial M?nima (SMO), possibilitando a resolu??o da complexa otimiza??o da SVM atrav?s de passos simples. A implementa??o da SMO tamb?m ? feita de modo extremamente paralelo, fazendo uso de t?cnicas para acelera??o como a cache do erro. Ademais, o Kernel Amig?vel ao Hardware (HFK) ? utilizado para diminuir a ?rea utilizada pelo kernel, permitindo que um n?mero maior de kernels seja implementado em um chip de mesmo tamanho, acelerando o treinamento. Ap?s a implementa??o paralela em hardware, a SVM ? validada por simula??o e s?o feitas an?lises associadas ao desempenho temporal da estrutura proposta, assim como an?lises associadas ao uso de ?rea da FPGA.<br>The importance of Field-Programmable Gate Arrays as compute accelerators has dramatically increased during the last couple of yers. Many companies such as Amazon, IBM and Microsoft included FPGAs in their data centers aiming to accelerate their search engines. In the center of those applications are many machine learning algorithms, such as Support Vector Machines (SVMs). For FPGAs to thrive in this new role, the effective usage of FPGA resources is required. The project?s main goal is the parallel FPGA implementation of both the feed-forward phase of a Support Vector Machine as well as its training phase. The feed-forward phase (inference) is implemented using the polynomial kernel in a highly parallel way in order to obtain maximum throughput at the cost of some extra area. Moreover, the inference implementation is capable of computing both classification and regression using a single hardware. The training phase of the SVM is implemented using Sequential Minimal Optimization (SMO), which enables the resolution of a complex convex optimization problem using simple steps. The SMO implementation is also highly parallel and uses some acceleration techniques, such as the error cache. Moreover, the Hardware Friendly Kernel (HFK) is used in order to reduce the kernel?s area, enabling the increase in the number of kernels per area. After the parallel implementation in hardware, the SVM is validated by simulation. Finally, analysis associated with the temporal performance of the proposed structure, as well as analysis associated with FPGA?s area usage are performed.

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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 chunks of 1000, for example, to learn the 10,000). Sequential and multi-threaded chunking methods provide a way to run the SVM on large datasets while retaining accuracy. The multi-threaded distributed SVM described in this thesis is implemented using Java RMI, and has been developed to run on a network of multi-core/multi-processor computers.

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Cova, Riccardo. "Analisi di dati citofluorimetrici con tecniche di Data Mining." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4774/.

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Il citofluorimetro è uno strumento impiegato in biologia genetica per analizzare dei campioni cellulari: esso, analizza individualmente le cellule contenute in un campione ed estrae, per ciascuna cellula, una serie di proprietà fisiche, feature, che la descrivono. L’obiettivo di questo lavoro è mettere a punto una metodologia integrata che utilizzi tali informazioni modellando, automatizzando ed estendendo alcune procedure che vengono eseguite oggi manualmente dagli esperti del dominio nell’analisi di alcuni parametri dell’eiaculato. Questo richiede lo sviluppo di tecniche biochimiche per la marcatura delle cellule e tecniche informatiche per analizzare il dato. Il primo passo prevede la realizzazione di un classificatore che, sulla base delle feature delle cellule, classifichi e quindi consenta di isolare le cellule di interesse per un particolare esame. Il secondo prevede l'analisi delle cellule di interesse, estraendo delle feature aggregate che possono essere indicatrici di certe patologie. Il requisito è la generazione di un report esplicativo che illustri, nella maniera più opportuna, le conclusioni raggiunte e che possa fungere da sistema di supporto alle decisioni del medico/biologo.

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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 chunk). In addition, we moved the kernel calculation part in SVM classification to a graphics processing unit (GPU) which has zero scheduling overhead to create concurrent threads. In this thesis, we will take advantage of this GPU architecture to improve the classification performance of SVM.

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"Three-dimensional hand gesture recognition using a ZCam and an SVM-SMO classifier." IOWA STATE UNIVERSITY, 2010. http://pqdtopen.proquest.com/#viewpdf?dispub=1468148.

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Gu, Gaung-Hui, and 古光輝. "Ubiquitous and Robust Text-Independent Speaker Recognition and FPGA Implementation for SMO algorithm of SVM." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/86629603830179768114.

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碩士<br>國立成功大學<br>電機工程學系碩博士班<br>96<br>A novel architecture for ubiquitous and robust of text-independent speaker recognition based on SVM approach is proposed. In this architecture, multiple far-field microphones of configuration is adopted to receive the pervasive speech signals, and the distance effect between speaker and microphone is supposed to be ignored. Then the multi-channel speech signals are added together through a mixer. In a ubiquitous computing environment, the received speech signal is usually heavily corrupted by background noises. An SNR-aware subspace speech of enhancement approach is used as a pre-processing to enhance the mixed informational signal as well as suppressing the noise. Considering the text-independent speaker recognition, this proposed work applies multi-class support vectors machine (SVM) instead of using conventional Gaussian mixture models (GMMs). In our experiments, the speaker recognition rate up to 97.2% with the proposed ubiquitous architecture of speaker recognition system. Additionally, we proposed a hardware realization of speaker identification system based on sequential minimal optimization (SMO) algorithm of SVM. We also proposed more efficient method of cache table utilization, and intend to save more then one half of cache table space as well as to reduce processing time of kernel function. Moreover, the heuristics selection method of SMO algorithm is implemented into hardware design to reduce the training time. In our experiments, the training time can reduce 2.17 times less than non-use of heuristics selection method on PC. And our finding shows that the identification ratio up to 92.5% of accuracy and reduced 53% of training time in hardware implementation.

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Book chapters on the topic "SVM-SMO"

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Ahmed, Alfalahi, Ramdani Mohamed, and Bellafkih Mostafa. "Use SMO SVM, LDA for Poet Identification in Arabic Poetry." In Smart Data and Computational Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11914-0_18.

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Sun, Changyin, Jinya Song, Guofang Lv, and Hua Liang. "Nonlinear Systems Modeling Using LS-SVM with SMO-Based Pruning Methods." In Advances in Neural Networks – ISNN 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72383-7_73.

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Zhao, Hengping, and Jinshou Yu. "A Modified SMO Algorithm for SVM Regression and Its Application in Quality Prediction of HP-LDPE." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_79.

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Vyas, Sahil, Manish Kumar Mukhija, and Satish Kumar Alaria. "An Efficient Approach for Plant Leaf Species Identification Based on SVM and SMO and Performance Improvement." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6581-4_1.

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Merzoug, Amina, Nacéra Benamrane, and Abdelmalik Taleb-Ahmed. "Lesions Detection of Multiple Sclerosis in 3D Brian MR Images by Using Artificial Immune Systems and Support Vector Machines." In Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7544-7.ch033.

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This paper presents a segmentation method to detect multiple sclerosis (MS) lesions in brain MRI based on the artificial immune systems (AIS) and a support vector machines (SVM). In the first step, AIS is used to segment the three main brain tissues white matter, gray matter, and cerebrospinal fluid. Then the features were extracted and SVM is applied to detect the multiple sclerosis lesions based on SMO training algorithm. The experiments conducted on 3D brain MR images produce satisfying results.

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Rathi, Tarun, and Vadlamani Ravi. "Customer Lifetime Value Measurement using Machine Learning Techniques." In Encyclopedia of Business Analytics and Optimization. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5202-6.ch051.

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Customer Lifetime Value (CLV) is an important metric in relationship marketing approaches. There have always been traditional techniques like Recency, Frequency and Monetary Value (RFM), Past Customer Value (PCV) and Share-of-Wallet (SOW) for segregation of customers into good or bad, but these are not adequate, as they only segment customers based on their past contribution. CLV on the other hand calculates the future value of a customer over his or her entire lifetime, which means it takes into account the prospect of a bad customer being good in future and hence profitable for a company or organization. In this paper, we review the various models and different techniques used in the measurement of CLV. Towards the end we make a comparison of various machine learning techniques like Classification and Regression Trees (CART), Support Vector Machines (SVM), SVM using SMO, Additive Regression, K-Star Method and Multilayer Perception (MLP) for the calculation of CLV.

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Sharma, Sanur, and Anurag Jain. "Hybrid Ensemble Learning With Feature Selection for Sentiment Classification in Social Media." In Research Anthology on Applying Social Networking Strategies to Classrooms and Libraries. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-7123-4.ch064.

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This article presents a study on ensemble learning and an empirical evaluation of various ensemble classifiers and ensemble features for sentiment classification of social media data. The data was collected from Twitter in real-time using Twitter API and text pre-processing and ranking-based feature selection is applied to textual data. A framework for a hybrid ensemble learning model is presented where a combination of ensemble features (Information Gain and CHI-Squared) and ensemble classifier that includes Ada Boost with SMO-SVM and Logistic Regression has been implemented. The classification of Twitter data is performed where sentiment analysis is used as a feature. The proposed model has shown improvements as compared to the state-of-the-art methods with an accuracy of 88.2% with a low error rate.

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Joshi, Amol Avinash, and Rabia Musheer Aziz. "Soft Computing Techniques for Cancer Classification of Gene Expression Microarray Data: A Three-Phase Hybrid Approach." In Optimization Techniques for Decision-making and Information Security. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815196320124030010.

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Recently, many soft computing methods have been implemented to extract information from big data. A standardized format for evaluating the expression levels of thousands of genes is made available by DNA microarray technology. Cancers of several anatomical regions can be identified with the help of patterns developed by gene expressions in microarray technology. Since the microarray data is too huge to process due to the curse of dimensionality problem. Methodology: Therefore, in this chapter, a setup based on a hybrid machine learning framework using soft computing techniques for feature selection is designed and executed to eliminate unnecessary genes and identify important genes for the identification of cancer. In the first stage, the genes or the features are taken out with the aid of the higher-order Independent Component Analysis (ICA) technique. Then, a wrapper algorithm that is based on Spider Monkey Optimization (SMO) with Genetic Algorithm (GA) is used to find the set of genes that improve the classification accuracy of Naïve Bayes (NB) classifiers and Support Vector Machine (SVM). For comparison purposes, three other optimization techniques considered in this chapter are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Genetic Algorithm (GA). After the selection of relevant expressed genes, the most popular classifiers namely Naïve Bayes (NB) and Support Vector Machine (SVM)) are trained with selected genes, and in the end, the accuracy of classification is determined using test data. Result: The experimental results with five benchmark microarray datasets of cancer prove that Genetic Spider Monkey (GSM) is a more efficient approach to improve the classification performance with ICA for both classifiers.

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Conference papers on the topic "SVM-SMO"

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Hernandez, Raul Acosta, Marius Strum, Wang Jiang Chau, and Jose Artur Quilici Gonzalez. "The Multiple Pairs SMO: A modified SMO algorithm for the acceleration of the SVM training." In 2009 International Joint Conference on Neural Networks (IJCNN 2009 - Atlanta). IEEE, 2009. http://dx.doi.org/10.1109/ijcnn.2009.5178701.

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Ahmed, Alfalahi, Ramdani Mohamed, and Bellafkih Mostafa. "Authorship attribution in Arabic poetry using NB, SVM, SMO." In 2016 11th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE, 2016. http://dx.doi.org/10.1109/sita.2016.7772287.

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Peng, Peng, Qian-Li Ma, and Lei-Ming Hong. "The research of the parallel SMO algorithm for solving SVM." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212348.

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Wei, Wang, and Duan HongYu. "The research of SMO algorithm self-adaption improvement on SVM." In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN). IEEE, 2011. http://dx.doi.org/10.1109/iccsn.2011.6014362.

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Hao, Meng, Yan Tianhao, and Yuan Fei. "The SVM based on SMO optimization for Speech Emotion Recognition." In 2019 Chinese Control Conference (CCC). IEEE, 2019. http://dx.doi.org/10.23919/chicc.2019.8866463.

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Lee, Changki, HyunKi Kim, and Myung-Gil Jang. "Fixed-threshold SMO for Joint Constraint Learning Algorithm of Structural SVM." In the 31st annual international ACM SIGIR conference. ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390526.

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Prasojoe, Rahmat Ramadan, and Setyorini Setyorini. "SVM Parallel Concept Test with SMO Decomposition on Cancer Microarray Dataset." In 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. http://dx.doi.org/10.1109/icoict52021.2021.9527411.

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Lin, Juanxi, Mengnan Song, and Jinglu Hu. "An SMO Approach to Fast SVM for Classification of Large Scale Data." In 2014 International Conference on IT Convergence and Security (ICITCS). IEEE, 2014. http://dx.doi.org/10.1109/icitcs.2014.7021735.

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Ouamour, Siham, and Halim Sayoud. "Authorship attribution of ancient texts written by ten arabic travelers using a SMO-SVM classifier." In 2012 International Conference on Communications and Information Technology (ICCIT). IEEE, 2012. http://dx.doi.org/10.1109/iccitechnol.2012.6285841.

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Vyas, Sahil, Manish Kumar Mukhija, and Satish Kumar Alaria. "Design and analysis of new algorithm for plant leaf species identification using by SVM and SMO." In RECENT ADVANCES IN SCIENCES, ENGINEERING, INFORMATION TECHNOLOGY & MANAGEMENT. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0154299.

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