Academic literature on the topic 'Support Vector Classification (SVC)'
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Journal articles on the topic "Support Vector Classification (SVC)"
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Chang, Chih-Chung, and Chih-Jen Lin. "Training v-Support Vector Regression: Theory and Algorithms." Neural Computation 14, no. 8 (2002): 1959–77. http://dx.doi.org/10.1162/089976602760128081.
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We discuss the relation betweenɛ-support vector regression (ɛ-SVR) and v-support vector regression (v-SVR). In particular, we focus on properties that are different from those of C-support vector classification (C-SVC) andv-support vector classification (v-SVC). We then discuss some issues that do not occur in the case of classification: the possible range of ɛ and the scaling of target values. A practical decomposition method forv-SVR is implemented, and computational experiments are conducted. We show some interesting numerical observations specific to regression.
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Flower, Dr K. Little. "Text Classification from positive and unlabeled examples using Support Vector Machine (SVM)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27391.
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Support Vector Machines (SVMs) are a powerful machine learning algorithm that can be used for text classification. Traditional SVMs require both positive and negative examples to train the model. However, in many real-world scenarios, it can be difficult or expensive to obtain negative examples. This study explores the application of SVMs in textclassification when only positive and unlabeled examples are available. Theresults showed that the proposed approach achieved competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. The utilization of SVC in the proposed approach is twofold. First, the SVC model is used to classify theunlabeled examples as positive or negative. Second, the SVC model is used to select the positive examples that are added to the training set. Thisiterative process of training and selecting examples helps to improve the classification accuracy of the SVM model. The proposed approach is a promising method for text classification when only positive and unlabeled examples are available. Theapproach is effective in achieving competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. This work contributes to enhancing text classification techniques, particularly in situationswith resource constraints and challenging label acquisition. Keywords: Support Vector Machine(S VM), Text Classifications ,Text Mining, SVC, Supervised Methods
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Gu, Tian Hong, Wei Lv, Xia Shao, and Wen Cong Lu. "Detection of High Energy Materials Using Support Vector Classification." Advanced Materials Research 554-556 (July 2012): 1628–31. http://dx.doi.org/10.4028/www.scientific.net/amr.554-556.1628.
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Based on the element contents of N, O, H and C of objects detected by γ-ray resonance, support vector classification (SVC) method was used to construct the model for distinguishing high energy materials (HEMs) from ordinary ones. It was found that the accuracy of prediction was 95.9% based on the leave-one-out cross validation (LOOCV) test. The results indicated that the performance of SVC model is good enough to detect HEMs in the presence of ordinary materials for the purpose of security checking.
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Zhang, Bowen. "Using Logistic Regression and Support Vector Classification to Predict Cancer." Highlights in Science, Engineering and Technology 92 (April 10, 2024): 288–94. http://dx.doi.org/10.54097/bkvnxg90.
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This study investigates the application of machine learning (ML) algorithms in the early diagnosis of breast cancer, focusing on logistic regression and Support Vector Classification (SVC). Utilizing a dataset from Kaggle, which includes diverse clinical features from breast mass samples, the research conducts a comparative analysis of these models in terms of accuracy and interpretability. Our findings reveal that both logistic regression and SVC demonstrate high precision in distinguishing between benign and malignant tumors, with SVC showing a marginally superior performance due to its higher sensitivity and lower rate of false negatives. The study emphasizes the potential of ML in enhancing cancer diagnostic processes, highlighting the importance of non-invasive, cost-effective, and accurate diagnostic alternatives. It also addresses the challenges of model interpretability and the need for more transparent ML applications in clinical settings. This research paves the way for future advancements in medical diagnostics, offering promising directions for integrating ML algorithms into clinical decision-making and patient care.
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Xia, Ding, Huiming Tang, Sixuan Sun, Chunyan Tang, and Bocheng Zhang. "Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification." Remote Sensing 14, no. 11 (2022): 2707. http://dx.doi.org/10.3390/rs14112707.
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A landslide susceptibility model based on a metaheuristic optimization algorithm (germinal center optimization (GCO)) and support vector classification (SVC) is proposed and applied to landslide susceptibility mapping in the Three Gorges Reservoir area in this paper. The proposed GCO-SVC model was constructed via the following steps: First, data on 11 influencing factors and 292 landslide polygons were collected to establish the spatial database. Then, after the influencing factors were subjected to multicollinearity analysis, the data were randomly divided into training and testing sets at a ratio of 7:3. Next, the SVC model with 5-fold cross-validation was optimized by hyperparameter space search using GCO to obtain the optimal hyperparameters, and then the best model was constructed based on the optimal hyperparameters and training set. Finally, the best model acquired by GCO-SVC was applied for landslide susceptibility mapping (LSM), and its performance was compared with that of 6 popular models. The proposed GCO-SVC model achieved better performance (0.9425) than the genetic algorithm support vector classification (GA-SVC; 0.9371), grid search optimized support vector classification (GRID-SVC; 0.9198), random forest (RF; 0.9085), artificial neural network (ANN; 0.9075), K-nearest neighbor (KNN; 0.8976), and decision tree (DT; 0.8914) models in terms of the area under the receiver operating characteristic curve (AUC), and the trends of the other metrics were consistent with that of the AUC. Therefore, the proposed GCO-SVC model has some advantages in LSM and may be worth promoting for wide use.
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Zhang, Chunhua, Xiaojian Shao, and Dewei Li. "Knowledge-based Support Vector Classification Based on C-SVC." Procedia Computer Science 17 (2013): 1083–90. http://dx.doi.org/10.1016/j.procs.2013.05.137.
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Chen, Yang, Lei Sun, Yangwen Huang, Lin Ou, and Ying Su. "Raman spectral statistical classification of nasopharyngeal carcinoma and nasopharyngeal normal cell lines based on support vector classification." Spectroscopy 26, no. 4-5 (2011): 231–36. http://dx.doi.org/10.1155/2011/672430.
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Raman spectroscopy (RS) has been used in the discrimination of normal and tumor cells for years. It is very important to validate an existing classification model using different algorithms. In this work, two algorithms of support vector classification (SVC) are utilized to validate our previous work about a LDA classification model of nasopharyngeal carcinoma (NPC) cell lines C666-1, CNE2 and nasopharyngeal normal cell line NP69. All of these two SVC algorithms use the same data set as the previous LDA model and, achieve great sensitivity and specificity. The final results show that our previous LDA classification model could be supported by different SVC algorithms and this demonstrates our classification model is reliable and may be helpful to the realization of RS to be one of diagnostic techniques of NPC.
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Li, Pengfei, Yongying Jiang, and Jiawei Xiang. "Experimental Investigation for Fault Diagnosis Based on a Hybrid Approach Using Wavelet Packet and Support Vector Classification." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/145807.
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To deal with the difficulty to obtain a large number of fault samples under the practical condition for mechanical fault diagnosis, a hybrid method that combined wavelet packet decomposition and support vector classification (SVC) is proposed. The wavelet packet is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the SVC. The rolling bearing and gear fault diagnostic results of the typical experimental platform show that the present approach is robust to noise and has higher classification accuracy and, thus, provides a better way to diagnose mechanical faults under the condition of small fault samples.
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Du, Qing Zhi. "Support Vector Linearly Inseparable Algorithm and its Optimizing Microwave Calcining Technology of Ammonium Uranyl Carbonate." Advanced Materials Research 739 (August 2013): 177–82. http://dx.doi.org/10.4028/www.scientific.net/amr.739.177.
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Support vector machines (SVMs) are a promising type of learning machine based on structural risk minimization and statistical learning theory, which can be divided into two categories: support vector classification (SVC) machines and support vector regression machines (SVR). The basic elements and algorithms of SVC machines are discussed. As modeling and prediction methods are introduced into the experiment of microwave calcining AUC, the better prediction accuracy and the better fitting results are compare with back propagation (BP) neural network method. This is conducted to elucidate the good generalization performance of SVMs, especially good for dealing with the data of some nonlinearity.
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Pham, Quoc, Tao-Chang Yang, Chen-Min Kuo, Hung-Wei Tseng, and Pao-Shan Yu. "Combing Random Forest and Least Square Support Vector Regression for Improving Extreme Rainfall Downscaling." Water 11, no. 3 (2019): 451. http://dx.doi.org/10.3390/w11030451.
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A statistical downscaling approach for improving extreme rainfall simulation was proposed to predict the daily rainfalls at Shih-Men Reservoir catchment in northern Taiwan. The structure of the proposed downscaling approach is composed of two parts: the rainfall-state classification and the regression for rainfall-amount prediction. Predictors of classification and regression methods were selected from the large-scale climate variables of the NCEP reanalysis data based on statistical tests. The data during 1964–1999 and 2000–2013 were used for calibration and validation, respectively. Three classification methods, including linear discriminant analysis (LDA), random forest (RF), and support vector classification (SVC), were adopted for rainfall-state classification and their performances were compared. After rainfall-state classification, the least square support vector regression (LS-SVR) was used for rainfall-amount prediction for different rainfall states. Two rainfall states (i.e., dry day and wet day) and three rainfall states (dry day, non-extreme-rainfall day, and extreme-rainfall day) were defined and compared for judging their downscaling performances. The results show that RF outperforms LDA and SVC for rainfall-state classification. Using RF for three-rainfall-states classification and LS-SVR for rainfall-amount prediction can improve the extreme rainfall downscaling.
Dissertations / Theses on the topic "Support Vector Classification (SVC)"
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Beltrami, Monica. "Método Grid-Quadtree para seleção de parâmetros do algoritmo support vector classification (SVC)." reponame:Repositório Institucional da UFPR, 2016. http://hdl.handle.net/1884/44061.
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Orientador : Prof. Dr. Arinei Carlos Lindbeck da Silva<br>Tese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 01/06/2016<br>Inclui referências : f. 143-149<br>Área de concentração : Programação matemática<br>Resumo: O algoritmo Support Vector Classification (SVC) é uma técnica de reconhecimento de padrões, cuja eficiência depende da seleção de seus parâmetros: constante de regularização C, função kernel e seus respectivos parâmetros. A escolha equivocada dessas variáveis impacta diretamente na performance do algoritmo, acarretando em fenômenos indesejáveis como o overfitting e o underfitting. O problema que estuda a procura de parâmetros ótimos para o SVC, em relação às suas medidas de desempenho, é denominado seleção de modelos do SVC. Em virtude do amplo domínio de convergência do kernel gaussiano, a maioria dos métodos destinados a solucionar esse problema concentra-se na seleção da constante C e do parâmetro ? do kernel gaussiano. Dentre esses métodos, a busca por grid é um dos de maior destaque devido à sua simplicidade e bons resultados. Contudo, por avaliar todas as combinações de parâmetros (C, ?) dentre o seu espaço de busca, a mesma necessita de muito tempo de processamento, tornando-se impraticável para avaliação de grandes conjuntos de dados. Desta forma, o objetivo deste trabalho é propor um método de seleção de parâmetros do SVC, usando o kernel gaussiano, que combine a técnica quadtree à busca por grid, para reduzir o número de operações efetuadas pelo grid e diminuir o seu custo computacional. A ideia fundamental é empregar a quadtree para desenhar a boa região de parâmetros, evitando avaliações desnecessárias de parâmetros situados nas áreas de underfitting e overfitting. Para isso, desenvolveu-se o método grid-quadtree (GQ), utilizando-se a linguagem de programação VB.net em conjunto com os softwares da biblioteca LIBSVM. Na execução do GQ, realizou-se o balanceamento da quadtree e criou-se um procedimento denominado refinamento, que permitiu delinear a curva de erro de generalização de parâmetros. Para validar o método proposto, empregaram-se vinte bases de dados referência na área de classificação, as quais foram separadas em dois grupos. Os resultados obtidos pelo GQ foram comparados com os da tradicional busca por grid (BG) levando-se em conta o número de operações executadas por ambos os métodos, a taxa de validação cruzada (VC) e o número de vetores suporte (VS) associados aos parâmetros encontrados e a acurácia do SVC na predição dos conjuntos de teste. A partir das análises realizadas, constatou-se que o GQ foi capaz de encontrar parâmetros de excelente qualidade, com altas taxas VC e baixas quantidades de VS, reduzindo em média, pelo menos, 78,8124% das operações da BG para o grupo 1 de dados e de 71,7172% a 88,7052% para o grupo 2. Essa diminuição na quantidade de cálculos efetuados pelo quadtree resultou em uma economia de horas de processamento. Além disso, em 11 das 20 bases estudadas a acurácia do SVC-GQ foi superior à do SVC-BG e para quatro delas igual. Isso mostra que o GQ é capaz de encontrar parâmetros melhores ou tão bons quanto os da BG executando muito menos operações. Palavras-chave: Seleção de modelos do SVC. Kernel gaussiano. Quadtree. Redução de operações.<br>Abstract: The Support Vector Classification (SVC) algorithm is a pattern recognition technique, whose efficiency depends on its parameters selection: the penalty constant C, the kernel function and its own parameters. A wrong choice of these variables values directly impacts on the algorithm performance, leading to undesirable phenomena such as the overfitting and the underfitting. The task of searching for optimal parameters with respect to performance measures is called SVC model selection problem. Due to the Gaussian kernel wide convergence domain, many model selection approaches focus in determine the constant C and the Gaussian kernel ? parameter. Among these, the grid search is one of the highlights due to its easiest way and high performance. However, since it evaluates all parameters combinations (C, ?) on the search space, it requires high computational time and becomes impractical for large data sets evaluation. Thus, the aim of this thesis is to propose a SVC model selection method, using the Gaussian kernel, which integrates the quadtree technique with the grid search to reduce the number of operations performed by the grid and its computational cost. The main idea of this study is to use the quadtree to determine the good parameters region, neglecting the evaluation of unnecessary parameters located in the underfitting and the overfitting areas. In this regard, it was developed the grid-quadtree (GQ) method, which was implemented on VB.net development environment and that also uses the software of the LIBSVM library. In the GQ execution, it was considered the balanced quadtree and it was created a refinement procedure, that allowed to delineate the parameters generalization error curve. In order to validate the proposed method, twenty benchmark classification data set were used, which were separated into two groups. The results obtained via GQ were compared with the traditional grid search (GS) ones, considering the number of operations performed by both methods, the cross-validation rate (CV) and the number of support vectors (SV) associated to the selected parameters, and the SVC accuracy in the test set. Based on this analyzes, it was concluded that GQ was able to find excellent parameters, with high CV rates and few SV, achieving an average reduction of at least 78,8124% on GS operations for group 1 data and from 71,7172% to 88,7052% for group 2. The decrease in the amount of calculations performed by the quadtree lead to savings on the computational time. Furthermore, the SVC-GQ accuracy was superior than SVC-GS in 11 of the 20 studied bases and equal in four of them. These results demonstrate that GQ is able to find better or as good as parameters than BG, but executing much less operations. Key words: SVC Model Selection. Gaussian kernel. Quadtree. Reduction Operations
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Rogers, Spencer David. "Support Vector Machines for Classification and Imputation." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3215.
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Support vector machines (SVMs) are a powerful tool for classification problems. SVMs have only been developed in the last 20 years with the availability of cheap and abundant computing power. SVMs are a non-statistical approach and make no assumptions about the distribution of the data. Here support vector machines are applied to a classic data set from the machine learning literature and the out-of-sample misclassification rates are compared to other classification methods. Finally, an algorithm for using support vector machines to address the difficulty in imputing missing categorical data is proposed and its performance is demonstrated under three different scenarios using data from the 1997 National Labor Survey.
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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 has advantages in that it offers greater degrees of freedom than the LDA for working with complex data sets. In addition, the SVM has direct methods to limit overfitting for the training sets (unlike the NN method). This thesis develops an SVM classifier to characterize the linearly non separable sleep and rest behaviors using a variety of features extracted from the power spectrum, autocorrelation function, and generalized spectrum (autocorrelation of complex spectrum). A genetic algorithm (GA) optimizes the SVM parameters and determines a combination of 5 best features. Experimental results from over 9 hours of data scored by human observation indicate 75% classification accuracy for SVM compared to 68% accuracy for LDA.
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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 by machine learning in which the classification is realized by looking at Internet traffic flow characteristics instead of actual payloads. Machine learning can solve the inherent limitations that DPI and port based classification suffers from. In this study the Internet traffic is divided into two classes of interest: Video and Other. There exist several machine learning methods for classification, and this study focuses on Support Vector Machine (SVM) to classify traffic. Several traffic characteristics are extracted, such as individual payload sizes and the longest consecutive run of payload packets in the downward direction. Several experiments using different approaches are conducted and the achieved results show that overall accuracies above 90% are achievable.<br>HITS, 4707
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Viau, Claude. "Multispectral Image Analysis for Object Recognition and Classification." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34532.
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Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate some form of decision-making process. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various field including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance.
The fundamental objectives of this research project were to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. The goal was not to find a new way to “fuse” the visual and thermal images together but rather establish a methodology to extract multispectral descriptors in order to improve a machine vision system’s ability to recognize specific classes of objects.A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM’s class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets. Commonly used performance metrics were applied to assess the sensitivity, specificity and accuracy of each classifier.
The research demonstrated that the highest recognition rate was achieved by an expert system (multiple classifiers) that combined the expertise of the visual-only classifier, the thermal-only classifier and the combined visual-thermal classifier.
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Plis, Kevin A. "The Effects of Novel Feature Vectors on Metagenomic Classification." Ohio University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1399578867.
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Huss, Jakob. "Cross Site Product Page Classification with Supervised Machine Learning." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189555.
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This work outlines a possible technique for identifying webpages that contain product specifications. Using support vector machines a product web page classifier was constructed and tested with various settings. The final result for this classifier ended up being 0.958 in precision and 0.796 in recall for product pages. The scores imply that the method could be considered a valid technique in real world web classification tasks if additional features and more data were made available.
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Höglind, Sanna, and Emelie Sundström. "Klassificering av transkriberade telefonsamtal med Support Vector Machines för ökad effektivitet inom vården." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262043.
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Patientnämndens förvaltning i Stockholm tar årligen emot tusentals samtal som önskar framföra klagomål på vården i Region Stockholm. Syftet med arbetet är att undersöka hur en NLP-robot för klassificering av inkomna klagomål skulle kunna bidra till en ökad effektivitet av verksamheten. Klassificeringen av klagomålen har utförts med hjälp av en metod baserad på Support Vector Machines. För att optimera modellens korrekthet undersöktes hur längden av ordvektorerna påverkar korrektheten. Modellen gav en slutgiltig korrekthet 53,10 %. Detta resultat analyserades sedan med målsättningen att identifiera potentiella förbättringsmöjligheter hos modellen. För framtida arbeten kan det därför vara intressant att undersöka hur antalet samtal, antalet personer som spelar in samtal och klassfördelningen i datamängden påverkar korrektheten. För att undersöka hur effektiviteten hos Patientnämndens förvaltning i Stockholm skulle påverkas av implementeringen av en NLP-robot användes en SWOT-analys. Denna analys visade på tydliga fördelar med automatisering av klagomålshanteringen, men att en sådan implementation måste ske med försiktighet där det säkerställs att tillgången på kompetens är tillräcklig för att förebygga potentiella hot.<br>Every year Patientnämnden recieves thousands of phone calls from patients wishing to make complaints about the health care in Stockholm. The aim of this work is to investigate how an NLP-robot for classification of recieved phone calls would contribute to an increased efficiency of the operation. The classification of the complaints has been made using a method based on Support Vector Machines. In order to optimize the accuracy of the model the impact of the length of the word vector has been investigated. The final result was an accuracy of 53.10%. The result was analyzed with the goal to identify potential opportunities of improvement of the model. For future work it could be interesting to investigate in how the number of calls, the number of people recording the calls and the distribution between the classes affect the accuracy A SWOT-analysis was performed in order to investigate in how the efficiency of Patientnämnden would be affected by the implementation of an NLP-robot. The analysis showed apparent benefits of automation of complaint management, but also that such an implementation must be done with great caution in order to be able to ensure that the available competence is high enough to prevent potential threats.
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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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Jabali, Aghyad, and Husein Abdelkadir Mohammedbrhan. "Tyre sound classification with machine learning." Thesis, Högskolan i Gävle, Datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-36209.
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Having enough data about the usage of tyre types on the road can lead to a better understanding of the consequences of studded tyres on the environment. This paper is focused on training and testing a machine learning model which can be further integrated into a larger system for automation of the data collection process. Different machine learning algorithms, namely CNN, SVM, and Random Forest, were compared in this experiment. The method used in this paper is an empirical method. First, sound data for studded and none-studded tyres was collected from three different locations in the city of Gävle/Sweden. A total of 760 Mel spectrograms from both classes was generated to train and test a well-known CNN model (AlexNet) on MATLAB. Sound features for both classes were extracted using JAudio to train and test models that use SVM and Random Forest classifi-ers on Weka. Unnecessary features were removed one by one from the list of features to improve the performance of the classifiers. The result shows that CNN achieved accuracy of 84%, SVM has the best performance both with and without removing some audio features (i.e 94% and 92%, respectively), while Random Forest has 89 % accuracy. The test data is comprised of 51% of the studded class and 49% of the none-studded class and the result of the SVM model has achieved more than 94 %. Therefore, it can be considered as an acceptable result that can be used in practice.
Books on the topic "Support Vector Classification (SVC)"
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Abe, Shigeo. Support Vector Machines for Pattern Classification. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-098-4.
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Stoean, Catalin, and Ruxandra Stoean. Support Vector Machines and Evolutionary Algorithms for Classification. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06941-8.
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Drezet, P. Directly optimized support vector machines for classification and regression. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.
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Support Vector Machines for Pattern Classification. Springer-Verlag, 2005. http://dx.doi.org/10.1007/1-84628-219-5.
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Abe, Shigeo. Support Vector Machines for Pattern Classification. Springer London, Limited, 2005.
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Abe, Shigeo. Support Vector Machines for Pattern Classification. Springer London, Limited, 2012.
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Abe, Shigeo. Support Vector Machines for Pattern Classification (Advances in Pattern Recognition). Springer, 2005.
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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.
Find full textBook chapters on the topic "Support Vector Classification (SVC)"
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Ye, Zijian, and Yi Mou. "Crayfish Quality Analysis Based on SVM and Infrared Spectra." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_99.
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AbstractDifferent algorithms combined with Near-infrared spectroscopy were investigated for the detection and classification of crayfish quality. In this study, the crawfish quality was predicted by partial least square-support vector machine, principal component analysis-support vector machine, BP neural network and support vector machine after pre-processing the NIR spectral data of crawfish. The result shows that the accuracy of near-infrared spectroscopy technology combined with SVM to classify crayfish quality can reach 100%, and the prediction can guide the sampling of crayfish food safety in practice, thus improving food safety and quality.
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Shilton, Alistair, and Marimuthu Palaniswami. "A Unified Approach to Support Vector Machines." In Pattern Recognition Technologies and Applications. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-807-9.ch014.
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This chapter presents a unified introduction to support vector machine (SVM) methods for binary classification, one-class classification, and regression. The SVM method for binary classification (binary SVC) is introduced first, and then extended to encompass one-class classification (clustering). Next, using the regularized risk approach as a motivation, the SVM method for regression (SVR) is described. These methods are then combined to obtain a single unified SVM formulation that encompasses binary classification, one-class classification, and regression (as well as some extensions of these), and the dual formulation of this unified model is derived. A mechanical analogy for binary and one-class SVCs is given to give an intuitive explanation of the operation of these two formulations. Finally, the unified SVM is extended to implement general cost functions, and an application of SVM classifiers to the problem of spam e-mail detection is considered.
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Xu Zhenyuan, Wu Mingnan, Watada Junzo, Ibrahim Zuwarie, and Khalid Marzuki. "Solving Imbalance Data Classification Problem by Particle Swarm Optimization Support Vector Machine." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-264-6-371.
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A database has a plenty of hidden knowledge, which can be used in decision making to support commerce, research and other activities. Classification analysis performs a very important rule in pattern recognition field as one core research topics. Algorithms like support vector machine (SVM) and artificial network (ANN) have been proposed to perform binary classification according to the distribution. But these traditional classification algorithms can hardly performs the satisfied result for imbalanced dataset. In this paper, we proposed to perform a model on the basis of Particle Swarm Optimization (PSO) and support vector machine (SVM) for a large imbalanced dataset. This model is named PSOSVC (Particle Swarm Optimization support vector classification) model. Recently, PSO is proposed used as a meta heuristic frame work for the large imbalanced classification. The SVM also shows high performance in balanced binary classification, so a novel model combined both support vector classification (SVC) and PSO is introduced to improve the classification accuracy. In this paper, G-mean is used to evaluate the final result. Performance in the final part of this paper the proposed method is compared with some conventional models, the results will show the high performance for imbalanced dataset classification by using the proposed method.
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Chopra, Deepti, and Roopal Khurana. "Support Vector Machine." In Introduction to Machine Learning with Python. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124422123010006.
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Support Vector Machine (SVM) may be defined as a machine learning algorithm that can be used for regression and classification. It is generally used for classification purposes. In this chapter, we will discuss Margin and Large Margin Methods and Kernel Methods.
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Ladwani, Vandana M. "Support Vector Machines and Applications." In Computer Vision. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5204-8.ch057.
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Support Vector Machines is one of the powerful Machine learning algorithms used for numerous applications. Support Vector Machines generate decision boundary between two classes which is characterized by special subset of the training data called as Support Vectors. The advantage of support vector machine over perceptron is that it generates a unique decision boundary with maximum margin. Kernalized version makes it very faster to learn as the data transformation is implicit. Object recognition using multiclass SVM is discussed in the chapter. The experiment uses histogram of visual words and multiclass SVM for image classification.
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Shawkat Ali, A. B. M. "Support Vector Machine." In Handbook of Research on Modern Systems Analysis and Design Technologies and Applications. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-887-1.ch028.
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From the beginning, machine learning methodology, which is the origin of artificial intelligence, has been rapidly spreading in the different research communities with successful outcomes. This chapter aims to introduce for system analysers and designers a comparatively new statistical supervised machine learning algorithm called support vector machine (SVM). We explain two useful areas of SVM, that is, classification and regression, with basic mathematical formulation and simple demonstration to make easy the understanding of SVM. Prospects and challenges of future research in this emerging area are also described. Future research of SVM will provide improved and quality access to the users. Therefore, developing an automated SVM system with state-of-the-art technologies is of paramount importance, and hence, this chapter will link up an important step in the system analysis and design perspective to this evolving research arena.
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Sun, Minghe. "Support Vector Machine Models for Classification." In Encyclopedia of Business Analytics and Optimization. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5202-6.ch215.
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As machine learning techniques, support vector machines are quadratic programming models and are recent revolutionary development for classification analysis. Primal and dual formulations of support vector machine models for both two-class and multi-class classification are discussed. The dual formulations in high dimensional feature space using inner product kernels are emphasized. Nonlinear classification function or discriminant functions in high dimensional feature spaces can be constructed through the use of inner product kernels without actually mapping the data from the input space to the high dimensional feature spaces. Furthermore, the size of the dual formulation is independent of the dimension of the input space and independent of the kernels used. Two illustrative examples, one for two-class and the other for multi-class classification, are used to demonstrate the formulations of these SVM models.
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Ben Youssef, Youssef, Elhassane Abdelmounim, and Abdelaziz Belaguid. "Mammogram Classification Using Support Vector Machine." In Cognitive Analytics. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2460-2.ch046.
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Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.
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Ben Youssef, Youssef, Elhassane Abdelmounim, and Abdelaziz Belaguid. "Mammogram Classification Using Support Vector Machine." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0773-4.ch019.
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Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.
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Mohsen, Farida, Md Rafiul Biswas, Hazrat Ali, Tanvir Alam, Mowafa Househ, and Zubair Shah. "Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220779.
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This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.
Conference papers on the topic "Support Vector Classification (SVC)"
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Miraliakbar, Alireza, and Zheyu Jiang. "Fast, Accurate, and Robust Fault Detection and Diagnosis of Industrial Processes." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.184473.
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Modern industrial processes are continuously monitored by a large number of sensors. Despite having access to large volumes of historical and online sensor data, industrial practitioners still face challenges in the era of Industry 4.0 in effectively utilizing them to perform online process monitoring and fast fault detection and diagnosis. To target these challenges, in this work, we present a novel framework named �FARM� for Fast, Accurate, and Robust online process Monitoring. FARM is a holistic monitoring framework that integrates (a) advanced multivariate statistical process control (SPC) for fast anomaly detection of nonparametric, heterogeneous data streams, and (b) modified support vector machine (SVM) for accurate and robust fault classification. Unlike existing general-purpose process monitoring frameworks, FARM�s unique hierarchical architecture decomposes process monitoring into two fault detection and diagnosis, each of which is conducted by targeted algorithms. Here, we test and validate the performance of our FARM monitoring framework on Tennessee Eastman Process (TEP) benchmark dataset. We show that SPC achieves faster fault detection speed at a lower false alarm rate compared to state-of-the-art benchmark fault detection methods. In terms of fault classification diagnosis, we show that our modified SVM algorithm successfully classifies 17 out of 20 of the fault scenarios present in the TEP dataset. Compared with the results of standard SVM trained directly on the original dataset, our modified SVM improves the fault classification accuracy significantly.
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Tohme, Mireille, and Regis Lengelle. "F-SVC: A simple and fast training algorithm soft margin Support Vector Classification." In 2008 IEEE Workshop on Machine Learning for Signal Processing (MLSP) (Formerly known as NNSP). IEEE, 2008. http://dx.doi.org/10.1109/mlsp.2008.4685503.
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Mittal, Harshit. "Evaluating The Performance of Feature Extraction Techniques Using Classification Techniques." In 4th International Conference on NLP Trends & Technologies. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131402.
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Dimensionality reduction techniques are widely used in machine learning to reduce the computational complexity of the model and improve its performance by identifying the most relevant features. In this research paper, we compare various dimensionality reduction techniques, including Principal Component Analysis(PCA), Independent Component Analysis(ICA), Local Linear Embedding(LLE), Local Binary Patterns(LBP), and Simple Autoencoder, on the Olivetti dataset, which is a popular benchmark dataset in the field of face recognition. We evaluate the performance of these dimensionality reduction techniques using various classification algorithms, including Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The goal of this research is to determine which combination of dimensionality reduction technique and classification algorithm is the most effective for the Olivetti dataset. Our research provides insights into the performance of various dimensionality reduction techniques and classification algorithms on the Olivetti dataset. These results can be useful in improving the performance of face recognition systems and other applications that deal with high-dimensional data.
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Fan, XiaoJing, LaiBin Zhang, Wei Liang, and ZhaoHui Wang. "Leak Detection Method Based on Support Vector Machine." In 2008 7th International Pipeline Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/ipc2008-64118.
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Assumptive and uncertain factors, few leak samples, complex non-linear pipeline systems are the problems often involved in the process of pipeline leak detection. Furthermore, the pressure wave changes of leakage are similar to these of valve regulation and pump closure. Thus it is difficult to establish a reliable model and to distinguish the leak signal pattern from others in pipeline leak detection. The veracity of leak detection system is limited. This paper presents a novel technique based on the statistical learning theory, support vector machine (SVM) for pipeline leak detection. Support Vector Machine (SVM) is learning system that uses a hypothesis space of linear functions in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional techniques. Thus, SVM has good performance for classification over small sample set. In this paper, an overview of the limitations of traditional statistics and the advantage of statistical learning theory will be introduced. In this paper, an SVM classifier is used to classify the signal pattern with few samples. Firstly, the algorithm of the SVM classifier and steps of using the model to identify leakage signals are studied. Secondly, the classification results of the experiment show that SVM classifier has high recognition accuracy. In addition, SVM is compared with neural network method. Then the paper concludes that in terms of classification ability and generalization performance, SVM has clearly advantages than neural network method over small sample set, so SVM is more applicable to pipeline leak detection.
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Liang, Peifeng, Weite Li, Yudong Wang, and Jinglu Hu. "One-Class Classification Using Quasi-Linear Support Vector Machine." In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2018. http://dx.doi.org/10.1109/smc.2018.00121.
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Benito, Ines, and Njuki W. Mureithi. "Identification of Two-Phase Flow Patterns Using Support Vector Classification." In ASME 2017 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/pvp2017-65179.
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Two-phase flows are preponderant in industrial components. The present work deals with external two-phase flows across tube banks commonly found in heat exchangers, boilers and steam generators. The flows are generally highly complex and remain theoretically intractable in most cases. The two-phase flow patterns provide a convenient albeit qualitative means for describing and classifying two phase flows. The flow patterns are also closely correlated to fluid-structure interaction dynamics and thus provide a practically useful basis for the study of two-phase flow-induced vibrations. For internal two-phase flows, maps by Taitel et al. (1980) and others have led to detailed and well defined maps. For transverse flows in tube bundles, there is significantly less agreement on the flow patterns and governing parameters. The complexity of flow in tube arrays is an obvious challenge. A second difficulty is the definition of distinct flow patterns and the identification of parameters uniquely identifying the flow patterns. The present work addresses the problem of two-phase flow pattern identification in tube arrays. Flow measurements using optical as well as flow visualization via high-speed videos and photography have been conducted. To identify the flow patterns, an artificial intelligence machine learning approach was taken. Pattern classification was achieved by designing a support vector machine (SVM) classifier. The SVM achieves quantitative and non-subjective classification by mapping the flow patterns in a high dimensional mathematical space in which the different flow patterns have unique characteristics. Details of the flow measurement, parameter definition and SVM design are presented in the paper. Flow patterns identified using the SVM are presented and compared with previously identified flow patterns.
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Ashwini, S., Megha Sinha, and C. Sabarinathan. "Implementation of Intrusion Detection Model for Detecting Cyberattacks Using Support Vector Machine." In International Research Conference on IOT, Cloud and Data Science. Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-6nyqo1.
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A Cyber-attack is a deliberate intent to take illegal access to one’s computer and data. The ascent of the web has turned into the groundwork of the vast majority's day-to-day schedules, and online administration has raised security worries. The rising measure of information, dividing among the cloud and the clients, additionally makes an attack surface. The attack surface has likewise extended with the ascent of organizations and the rising number of individuals utilizing them. The capacity of existing discovery plans to approve the goal and the earlier acknowledgment of assaults is falling apart. In the event that no effective assurance mechanism is carried out, the web will turn out to be substantially more helpless, expanding the gamble of information spillage or hacking. The focus here is to put forward a model (IDS) that detects network intrusions or anomaly detection by classifying all the network traffic packets as non-attack (harmless) or attack (vindictive) classes and also classifying the type of malicious classes using Support Vector Machine algorithm. The machine learning algorithm Support Vector Machine works for classification as well as regression problems. Decision boundaries are usually used in Support Vector Classification (SVC). We have used two different datasets of cybersecurity, namely KDDCUP 1999 and UNSW_NB15. The proposed model has been evaluated using performance metrics, namely accuracy, precision, recall (Detection rate), and F-measure. The test results exhibit that our framework has better identification execution for various cyberattacks. This model achieves an accuracy of 99.8 percent with the KDDCUP 1999 dataset and 98.2 percent with the UNSW_NB15 dataset, and remarkable detection rates of attacks.
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Seeland, Madeleine, Andreas Maunz, Andreas Karwath, and Stefan Kramer. "Extracting information from support vector machines for pattern-based classification." In SAC 2014: Symposium on Applied Computing. ACM, 2014. http://dx.doi.org/10.1145/2554850.2555065.
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Garcia, Marina Pinho, Giovana Pinho Garcia, and Nádia Félix Felipe da Silva. "Humor Detection using Support Vector Machine." In Escola Regional de Informática de Goiás. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/erigo.2021.18437.
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This paper aims classify texts in humorous and non-humorous, while exploring the different parameters and tactics that can be used alongside the Support Vector Machine (SVM) classifier, to see and understand their impact on the classification and find the best combinations that have the best performances considering the accuracy and the F1 score. After observing the plots and analyzing the data we were able to come to a conclusion of which combination would be best to classify the texts in the testing data provided by the HaHackathon: Detecting and Rating Humor and Offense CodaLab Competition [cod 2021]. With those results we were able to give a wide view of this type of problem solutions, which can be used in further related work in this field of research.
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Xu, Jie, Xianglong Liu, Zhouyuan Huo, Cheng Deng, Feiping Nie, and Heng Huang. "Multi-Class Support Vector Machine via Maximizing Multi-Class Margins." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/440.
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Support Vector Machine (SVM) is originally proposed as a binary classification model, and it has already achieved great success in different applications. In reality, it is more often to solve a problem which has more than two classes. So, it is natural to extend SVM to a multi-class classifier. There have been many works proposed to construct a multi-class classifier based on binary SVM, such as one versus all strategy, one versus one strategy and Weston's multi-class SVM. One versus all strategy and one versus one strategy split the multi-class problem to multiple binary classification subproblems, and we need to train multiple binary classifiers. Weston's multi-class SVM is formed by ensuring risk constraints and imposing a specific regularization, like Frobenius norm. It is not derived by maximizing the margin between hyperplane and training data which is the motivation in SVM. In this paper, we propose a multi-class SVM model from the perspective of maximizing margin between training points and hyperplane, and analyze the relation between our model and other related methods. In the experiment, it shows that our model can get better or compared results when comparing with other related methods.
Reports on the topic "Support Vector Classification (SVC)"
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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 features based on the statistical significance of classical univariate analysis (p<0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p<0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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Arun, Ramaiah, and Shanmugasundaram Singaravelan. Classification of Brain Tumour in Magnetic Resonance Images Using Hybrid Kernel Based Support Vector Machine. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2019. http://dx.doi.org/10.7546/crabs.2019.10.12.
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