Relevant bibliographies by topics / SVM classification

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

Academic literature on the topic 'SVM classification'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "SVM classification"

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Wang, Bo, Yu Kai Yao, Xiao Ping Wang, and Xiao Yun Chen. "PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM." Applied Mechanics and Materials 701-702 (December 2014): 58–62. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.58.

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As one of the most popular and effective classification algorithms, Support Vector Machine (SVM) has attracted much attention in recent years. Classifiers ensemble is a research direction in machine learning and statistics, it often gives a higher classification accuracy than the single classifier. This paper proposes a new ensemble algorithm based on SVM. The proposed classification algorithm PB-SVM Ensemble consists of some SVM classifiers produced by PCAenSVM and fifty classifiers trained using Bagging, the results are combined to make the final decision on testing set using majority voting. The performance of PB-SVM Ensemble are evaluated on six datasets which are from UCI repository, Statlog or the famous research. The results of the experiment are compared with LibSVM, PCAenSVM and Bagging. PB-SVM Ensemble outperform other three algorithms in classification accuracy, and at the same time keep a higher confidence of accuracy than Bagging.
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Subha, R., and M. Pushpa Rani. "SVM based Iris Classification." International Journal of Computer Sciences and Engineering 6, no. 2 (February 28, 2018): 321–23. http://dx.doi.org/10.26438/ijcse/v6i2.321323.

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Vaidya, Jaideep, Hwanjo Yu, and Xiaoqian Jiang. "Privacy-preserving SVM classification." Knowledge and Information Systems 14, no. 2 (March 24, 2007): 161–78. http://dx.doi.org/10.1007/s10115-007-0073-7.

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Gu, Suicheng, and Yuhong Guo. "Learning SVM Classifiers with Indefinite Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 942–48. http://dx.doi.org/10.1609/aaai.v26i1.8293.

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Recently, training support vector machines with indefinite kernels has attracted great attention in the machine learning community. In this paper, we tackle this problem by formulating a joint optimization model over SVM classifications and kernel principal component analysis. We first reformulate the kernel principal component analysis as a general kernel transformation framework, and then incorporate it into the SVM classification to formulate a joint optimization model. The proposed model has the advantage of making consistent kernel transformations over training and test samples. It can be used for both binary classification and multi-class classification problems. Our experimental results on both synthetic data sets and real world data sets show the proposed model can significantly outperform related approaches.
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Bansal, Esha, and Anupam Bhatia. "Kernel’s Impact on SVM Classification." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 5 (May 30, 2017): 359–62. http://dx.doi.org/10.23956/ijarcsse/sv7i5/0238.

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Al-Khalidy, Joanne H., and Raid R. Al-Ne’ma. "Breast Tumor Classification Using SVM." Tikrit Journal of Engineering Sciences 21, no. 1 (July 18, 2013): 43–49. http://dx.doi.org/10.25130/tjes.21.1.06.

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Although there are several techniques that have been used to differentiate between benign andmalignant breast tumor lately, support vector machines (SVMs) have been distinguished as one ofthe common method of classification for many fields such as medical diagnostic, that it offersmany advantages with respect to previously proposed methods such as ANNs. One of them is thatSVM provide a higher accuracy, another advantage that SVM reduces the computational cost,and it is already showed good result in this work.In this paper, a Support Vector Machine for differentiation Breast tumor was presented torecognize malignant or benign in mammograms. This work used 569 cases and they wereclassified into two groups: malignant (+1) or benign (-1), then randomly selected some of thesesamples for training model while others were used for test. The ratios were 84.4.0% of acceptedfalse, 947142% of refused false. These results indicate how much this method is successful.
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Ivanova, Vanya, Tasho Tashev, and Ivo Draganov. "DDoS Attacks Classification using SVM." WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 19 (February 9, 2022): 1–11. http://dx.doi.org/10.37394/23209.2022.19.1.

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In this paper two types of classifiers of Distributed Denial of Service (DDoS) attacks, based on Support Vector Machines, are presented – a binary and a multiclass one. They use numerical samples, aggregated from packet switched network connections records, captured between attacking machines, most typically IoT bots and a victim machine. Ten of the most popular DDoS attacks are studied and represented as either 10- or 8-feature vectors. Detection rate and classification accuracy is being measured in both cases, along with lots of other parameters, such as Precision, Recall, F1-measure, training and testing time, and others. Variations with Linear, Polynomial, RBF and Sigmoid kernels are being tried with the SVM. The most accurate turns out to be the RBF SVM, both as detector and multiclass classifier, achieving classification accuracy as high as 0.9999 for some of the attacks. Testing times reveal the practical fitness of the implemented classifiers for real-world application.
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Reynolds, Evan, Brian Callaghan, and Mousumi Banerjee. "SVM–CART for disease classification." Journal of Applied Statistics 46, no. 16 (June 7, 2019): 2987–3007. http://dx.doi.org/10.1080/02664763.2019.1625876.

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Sujitha, R., and B. Paramasivan. "Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification." Computers, Materials & Continua 70, no. 1 (2022): 1557–72. http://dx.doi.org/10.32604/cmc.2022.019323.

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Huang, Mei-Ling, Yung-Hsiang Hung, W. M. Lee, R. K. Li, and Bo-Ru Jiang. "SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/795624.

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Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parametersCandγto increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.
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Dissertations / Theses on the topic "SVM classification"

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MELONI, RAPHAEL BELO DA SILVA. "REMOTE SENSING IMAGE CLASSIFICATION USING SVM." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31439@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Classificação de imagens é o processo de extração de informação em imagens digitais para reconhecimento de padrões e objetos homogêneos, que em sensoriamento remoto propõe-se a encontrar padrões entre os pixels pertencentes a uma imagem digital e áreas da superfície terrestre, para uma análise posterior por um especialista. Nesta dissertação, utilizamos a metodologia de aprendizado de máquina support vector machines para o problema de classificação de imagens, devido a possibilidade de trabalhar com grande quantidades de características. Construímos classificadores para o problema, utilizando imagens distintas que contém as informações de espaços de cores RGB e HSB, dos valores altimétricos e do canal infravermelho de uma região. Os valores de relevo ou altimétricos contribuíram de forma excelente nos resultados, uma vez que esses valores são características fundamentais de uma região e os mesmos não tinham sido analisados em classificação de imagens de sensoriamento remoto. Destacamos o resultado final, do problema de classificação de imagens, para o problema de identificação de piscinas com vizinhança dois. Os resultados obtidos são 99 por cento de acurácia, 100 por cento de precisão, 93,75 por cento de recall, 96,77 por cento de F-Score e 96,18 por cento de índice Kappa.
Image Classification is an information extraction process in digital images for pattern and homogeneous objects recognition. In remote sensing it aims to find patterns from digital images pixels, covering an area of earth surface, for subsequent analysis by a specialist. In this dissertation, to this images classification problem we employ Support Vector Machines, a machine learning methodology, due the possibility of working with large quantities of features. We built classifiers to the problem using different image information, such as RGB and HSB color spaces, altimetric values and infrared channel of a region. The altimetric values contributed to excellent results, since these values are fundamental characteristics of a region and they were not previously considered in remote sensing images classification. We highlight the final result, for the identifying swimming pools problem, when neighborhood is two. The results have 99 percent accuracy, 100 percent precision, 93.75 percent of recall, 96.77 percent F-Score and 96.18 percent of Kappa index.
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Jiang, Fuhua. "SVM-Based Negative Data Mining to Binary Classification." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/8.

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The properties of training data set such as size, distribution and the number of attributes significantly contribute to the generalization error of a learning machine. A not well-distributed data set is prone to lead to a partial overfitting model. Two approaches proposed in this dissertation for the binary classification enhance useful data information by mining negative data. First, an error driven compensating hypothesis approach is based on Support Vector Machines (SVMs) with (1+k)-iteration learning, where the base learning hypothesis is iteratively compensated k times. This approach produces a new hypothesis on the new data set in which each label is a transformation of the label from the negative data set, further producing the positive and negative child data subsets in subsequent iterations. This procedure refines the base hypothesis by the k child hypotheses created in k iterations. A prediction method is also proposed to trace the relationship between negative subsets and testing data set by a vector similarity technique. Second, a statistical negative example learning approach based on theoretical analysis improves the performance of the base learning algorithm learner by creating one or two additional hypotheses audit and booster to mine the negative examples output from the learner. The learner employs a regular Support Vector Machine to classify main examples and recognize which examples are negative. The audit works on the negative training data created by learner to predict whether an instance is negative. However, the boosting learning booster is applied when audit does not have enough accuracy to judge learner correctly. Booster works on training data subsets with which learner and audit do not agree. The classifier for testing is the combination of learner, audit and booster. The classifier for testing a specific instance returns the learner's result if audit acknowledges learner's result or learner agrees with audit's judgment, otherwise returns the booster's result. The error of the classifier is decreased to O(e^2) comparing to the error O(e) of a base learning algorithm.
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Severini, Jérôme. "Estimation et Classification de Signaux Altimétriques." Thesis, Toulouse, INPT, 2010. http://www.theses.fr/2010INPT0125/document.

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La mesure de la hauteur des océans, des vents de surface (fortement liés aux températures des océans), ou encore de la hauteur des vagues sont un ensemble de paramètres nécessaires à l'étude des océans mais aussi au suivi de leurs évolutions : l'altimétrie spatiale est l'une des disciplines le permettant. Une forme d'onde altimétrique est le résultat de l'émission d'une onde radar haute fréquence sur une surface donnée (classiquement océanique) et de la mesure de la réflexion de cette onde. Il existe actuellement une méthode d'estimation non optimale des formes d'onde altimétriques ainsi que des outils de classifications permettant d'identifier les différents types de surfaces observées. Nous proposons dans cette étude d'appliquer la méthode d'estimation bayésienne aux formes d'onde altimétriques ainsi que de nouvelles approches de classification. Nous proposons enfin la mise en place d'un algorithme spécifique permettant l'étude de la topographie en milieu côtier, étude qui est actuellement très peu développée dans le domaine de l'altimétrie
After having scanned the ocean levels during thirteen years, the french/american satelliteTopex-Poséidon disappeared in 2005. Topex-Poséidon was replaced by Jason-1 in december 2001 and a new satellit Jason-2 is waited for 2008. Several estimation methods have been developed for signals resulting from these satellites. In particular, estimators of the sea height and wave height have shown very good performance when they are applied on waveforms backscattered from ocean surfaces. However, it is a more challenging problem to extract relevant information from signals backscattered from non-oceanic surfaces such as inland waters, deserts or ices. This PhD thesis is divided into two parts : A first direction consists of developing classification methods for altimetric signals in order to recognize the type of surface affected by the radar waveform. In particular, a specific attention will be devoted to support vector machines (SVMs) and functional data analysis for this problem. The second part of this thesis consists of developing estimation algorithms appropriate to altimetric signals obtained after reflexion on non-oceanic surfaces. Bayesian algorithms are currently under investigation for this estimation problem. This PhD is co-supervised by the french society CLS (Collect Localisation Satellite) (seehttp://www.cls.fr/ for more details) which will in particular provide the real altimetric data necessary for this study
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Almasiri, osamah A. "SKIN CANCER DETECTION USING SVM-BASED CLASSIFICATION AND PSO FOR SEGMENTATION." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5489.

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Various techniques are developed for detecting skin cancer. However, the type of maligned skin cancer is still an open problem. The objective of this study is to diagnose melanoma through design and implementation of a computerized image analysis system. The dataset which is used with the proposed system is Hospital Pedro Hispano (PH²). The proposed system begins with preprocessing of images of skin cancer. Then, particle swarm optimization (PSO) is used for detecting the region of interest (ROI). After that, features extraction (geometric, color, and texture) is taken from (ROI). Lastly, features selection and classification are done using a support vector machine (SVM). Results showed that with a data set of 200 images, the sensitivity (SE) and the specificity (SP) reached 100% with a maximum processing time of 0.03 sec.
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Tarasova, Natalya. "Classification of Hate Tweets and Their Reasons using SVM." Thesis, Uppsala universitet, Avdelningen för datalogi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-275782.

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Denna studie fokuserar på att klassificera hat-meddelanden riktade mot mobiloperatörerna Verizon,  AT&T and Sprint. Huvudsyftet är att med hjälp av maskininlärningsalgoritmen Support Vector Machines (SVM) klassificera meddelanden i fyra kategorier - Hat, Orsak, Explicit och Övrigt - för att kunna identifiera ett hat-meddelande och dess orsak. Studien resulterade i två metoder: en "naiv" metod (the Naive Method, NM) och en mer "avancerad" metod (the Partial Timeline Method, PTM). NM är en binär metod i den bemärkelsen att den ställer frågan: "Tillhör denna tweet klassen Hat?". PTM ställer samma fråga men till en begränsad mängd av tweets, dvs bara de som ligger inom ± 30 min från publiceringen av hat-tweeten. Sammanfattningsvis indikerade studiens resultat att PTM är noggrannare än NM. Dock tar den inte hänsyn till samtliga tweets på användarens tidslinje. Därför medför valet av metod en avvägning: PTM erbjuder en noggrannare klassificering och NM erbjuder en mer utförlig klassificering.
This study focused on finding the hate tweets posted by the customers of three mobileoperators Verizon, AT&T and Sprint and identifying the reasons for their dissatisfaction. The timelines with a hate tweet were collected and studied for the presence of an explanation. A machine learning approach was employed using four categories: Hate, Reason, Explanatory and Other. The classication was conducted with one-versus-all approach using Support Vector Machines algorithm implemented in a LIBSVM tool. The study resulted in two methodologies: the Naive method (NM) and the Partial Time-line Method (PTM). The Naive Method relied only on the feature space consisting of the most representative words chosen with Akaike Information Criterion. PTM utilized the fact that the majority of the explanations were posted within a one-hour time window of the posting of a hate tweet. We found that the accuracy of PTM is higher than for NM. In addition, PTM saves time and memory by analysing fewer tweets. At the same time this implies a trade-off between relevance and completeness.

Opponent: Kristina Wettainen

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Lekic, Sasa, and Kasper Liu. "Intent classification through conversational interfaces : Classification within a small domain." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-257863.

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Natural language processing and Machine learning are subjects undergoing intense study nowadays. These fields are continually spreading, and are more interrelated than ever before. A case in point is text classification which is an instance of Machine learning(ML) application in Natural Language processing(NLP).Although these subjects have evolved over the recent years, they still have some problems that have to be considered. Some are related to the computing power techniques from these subjects require, whereas the others to how much training data they require.The research problem addressed in this thesis regards lack of knowledge on whether Machine learning techniques such as Word2Vec, Bidirectional encoder representations from transformers (BERT) and Support vector machine(SVM) classifier can be used for text classification, provided only a small training set. Furthermore, it is not known whether these techniques can be run on regular laptops.To solve the research problem, the main purpose of this thesis was to develop two separate conversational interfaces utilizing text classification techniques. These interfaces, provided with user input, can recognise the intent behind it, viz. classify the input sentence within a small set of pre-defined categories. Firstly, a conversational interface utilizing Word2Vec, and SVM classifier was developed. Secondly, an interface utilizing BERT and SVM classifier was developed. The goal of the thesis was to determine whether a small dataset can be used for intent classification and with what accuracy, and if it can be run on regular laptops.The research reported in this thesis followed a standard applied research method. The main purpose was achieved and the two conversational interfaces were developed. Regarding the conversational interface utilizing Word2Vec pre-trained dataset, and SVM classifier, the main results showed that it can be used for intent classification with the accuracy of 60%, and that it can be run on regular computers. Concerning the conversational interface utilizing BERT and SVM Classifier, the results showed that this interface cannot be trained and run on regular laptops. The training ran over 24 hours and then crashed.The results showed that it is possible to make a conversational interface which is able to classify intents provided only a small training set. However, due to the small training set, and consequently low accuracy, this conversational interface is not a suitable option for important tasks, but can be used for some non-critical classification tasks.
Natural language processing och maskininlärning är ämnen som forskas mycket om idag. Dessa områden fortsätter växa och blir allt mer sammanvävda, nu mer än någonsin. Ett område är textklassifikation som är en gren av maskininlärningsapplikationer (ML) inom Natural language processing (NLP).Även om dessa ämnen har utvecklats de senaste åren, finns det fortfarande problem att ha i å tanke. Vissa är relaterade till rå datakraft som krävs för dessa tekniker medans andra problem handlar om mängden data som krävs.Forskningsfrågan i denna avhandling handlar om kunskapsbrist inom maskininlärningtekniker som Word2vec, Bidirectional encoder representations from transformers (BERT) och Support vector machine(SVM) klassificierare kan användas som klassification, givet endast små träningsset. Fortsättningsvis, vet man inte om dessa metoder fungerar på vanliga datorer.För att lösa forskningsproblemet, huvudsyftet för denna avhandling var att utveckla två separata konversationsgränssnitt som använder textklassifikationstekniker. Dessa gränssnitt, give med data, kan känna igen syftet bakom det, med andra ord, klassificera given datamening inom ett litet set av fördefinierade kategorier. Först, utvecklades ett konversationsgränssnitt som använder Word2vec och SVM klassificerare. För det andra, utvecklades ett gränssnitt som använder BERT och SVM klassificerare. Målet med denna avhandling var att avgöra om ett litet dataset kan användas för syftesklassifikation och med vad för träffsäkerhet, och om det kan användas på vanliga datorer.Forskningen i denna avhandling följde en standard tillämpad forskningsmetod. Huvudsyftet uppnåddes och de två konversationsgränssnitten utvecklades. Angående konversationsgränssnittet som använde Word2vec förtränat dataset och SVM klassificerar, visade resultatet att det kan användas för syftesklassifikation till en träffsäkerhet på 60%, och fungerar på vanliga datorer. Angående konversationsgränssnittet som använde BERT och SVM klassificerare, visade resultatet att det inte går att köra det på vanliga datorer. Träningen kördes i över 24 timmar och kraschade efter det.Resultatet visade att det är möjligt att skapa ett konversationsgränssnitt som kan klassificera syften, givet endast ett litet träningsset. Däremot, på grund av det begränsade träningssetet, och konsekvent låg träffsäkerhet, är denna konversationsgränssnitt inte lämplig för viktiga uppgifter, men kan användas för icke kritiska klassifikationsuppdrag.
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LI, YUANXUN. "SVM Object Based Classification Using Dense Satellite Imagery Time Series." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233340.

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Liu, Wen. "Incremental Learning and Online-Style SVM for Traffic Light Classification." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-theses/1216.

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Training a large dataset has become a serious issue for researchers because it requires large memories and can take a long time for computing. People are trying to process large scale dataset not only by changing programming model, such as using MapReduce and Hadoop, but also by designing new algorithms that can retain performance with less complexity and runtime. In this thesis, we present implementations of incremental learning and online learning methods to classify a large traffic light dataset for traffic light recognition. The introduction part includes the concepts and related works of incremental learning and online learning. The main algorithm is a modification of IMORL incremental learning model to enhance its performance over the learning process of our application. Then we briefly discuss how the traffic light recognition algorithm works and the problem we encounter during training. Rather than focusing on incremental learning, which uses batch to batch data during training procedure, we introduce Pegasos, an online style primal gradient-based support vector machine method. The performance of Pegasos for classification is extraordinary and the number of instances it uses for training is relatively small. Therefore, Pegasos is the recommended solution to the large dataset training problem.
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Nordström, Jesper. "Automated classification of bibliographic data using SVM and Naive Bayes." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-75167.

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Classification of scientific bibliographic data is an important and increasingly more time-consuming task in a “publish or perish” paradigm where the number of scientific publications is steadily growing. Apart from being a resource-intensive endeavor, manual classification has also been shown to be often performed with a quite high degree of inconsistency. Since many bibliographic databases contain a large number of already classified records supervised machine learning for automated classification might be a solution for handling the increasing volumes of published scientific articles. In this study automated classification of bibliographic data, based on two different machine learning methods; Naive Bayes and Support Vector Machine (SVM), were evaluated. The data used in the study were collected from the Swedish research database SwePub and the features used for training the classifiers were based on abstracts and titles in the bibliographic records. The accuracy achieved ranged between a lowest score of 0.54 and a highest score of 0.84. The classifiers based on Support Vector Machine did consistently receive higher scores than the classifiers based on Naive Bayes. Classification performed at the second level in the hierarchical classification system used clearly resulted in lower scores than classification performed at the first level. Using abstracts as the basis for feature extraction yielded overall better results than using titles, the differences were however very small.
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Shaik, Abdul Ameer Basha. "SVM Classification and Analysis of Margin Distance on Microarray Data." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924.

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Books on the topic "SVM classification"

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Kornevye paraziticheskie nematody sem. Tylenchorhynchidae mirovoĭ fauny. Vladivostok: Dalʹnauka, 1993.

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Ehrenberg, Maria. Sagans förvandlingar: Eva Wigström som sagosamlare och sagoförfattare. Stockholm: ETC förlag, 2003.

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Kurchenko, E. I. Rod polevit︠s︡a (Agrostis L., sem. Poaceae) Rossii i sopredelʹnykh stran: Morfologii︠a︡, sistematika i ėvoli︠u︡t︠s︡ionnye otnoshenii︠a︡ = Genus Agrostis L. (Poaceae) of Russia and neighbouring countries : Morphology, taxonomy and evolution relations. Moskva: Prometeĭ, 2010.

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M. W. S. J. M. van Slageren. A taxonomic monograph of the genera Brachiolejeunea and Frullanoides (Hepaticae), with a sem analysis of the sporophyte in the Ptychanthoideae. [Utrecht: Botanisch Museum en Herbarium van de Rijksuniversiteit te Utrecht], 1985.

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Stannius, Birgitte. UDK som fælles klassifikationssystem i danske forskningsbiblioteker: En analyse af Roskilde Universitetsbiblioteks brug af UDK i lyset af 1970ernes fællesklassifikationsdebat. København: Danmarks biblioteksskole, 1985.

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Sundland, Egil. Det var en gang--et menneske: Tolkninger av Asbjørnsen og Moes undereventyr som allegorier på menneskelig innsikt og erkjennelse. [Oslo]: Cappelen, 1995.

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

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Vasilʹevich, Kucherov Evgeniĭ, Muldashev A. A, Alekseev I͡U︡ E, and Institut biologii (Akademii͡a︡ nauk SSSR. Bashkirskiĭ nauchnyĭ t͡s︡entr), eds. Opredelitelʹ vysshikh rasteniĭ Bashkirskoĭ ASSR. Sem. Onocleacea-Fumariaceae. Moskva: "Nauka", 1988.

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Katritsis, Demosthenes G., Bernard J. Gersh, and A. John Camm. Epidemiology, presentation, and therapy of supraventricular tachycardias. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199685288.003.1095_update_001.

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Supraventricular tachycardias (SVT) are traditionally considered as sinus nodal tachycardias, atrial tachycardia and flutter, AVNRT and other junctional arrhythmias, and AVRT. In this chapter, classification, epidemiology, and presentation of SVT in various clinical settings are presented.
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Bisen, S. S. Identification and Classification of Indian Bamboos ; SEM Atlas of Epidermis. Bishen Singh Mahendra Pal Singh, 1999.

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Book chapters on the topic "SVM classification"

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Zhu, Yanwei. "SVM Classification Algorithm in ECG Classification." In Communications in Computer and Information Science, 797–803. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34041-3_110.

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Orchel, Marcin. "Incorporating Detractors into SVM Classification." In Man-Machine Interactions, 361–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00563-3_38.

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Chen, Mu-Song, Chipan Hwang, and Tze-Yee Ho. "Terrain Image Classification with SVM." In Lecture Notes in Computer Science, 89–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38715-9_11.

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Frossyniotis, Dimitrios S., and Andreas Stafylopatis. "A Multi-SVM Classification System." In Multiple Classifier Systems, 198–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_20.

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Imam, Tasadduq, Kai Ming Ting, and Joarder Kamruzzaman. "z-SVM: An SVM for Improved Classification of Imbalanced Data." In Lecture Notes in Computer Science, 264–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_30.

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Krey, Sebastian, and Uwe Ligges. "SVM Based Instrument and Timbre Classification." In Studies in Classification, Data Analysis, and Knowledge Organization, 759–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0_84.

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Behera, Sandhyalati, and Mihir Narayan Mohanty. "Classification of EEG Signal Using SVM." In Advances in Electrical Control and Signal Systems, 859–69. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5262-5_65.

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Yu, Hwanjo, and Sungchul Kim. "SVM Tutorial — Classification, Regression and Ranking." In Handbook of Natural Computing, 479–506. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-540-92910-9_15.

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Li, Zhanchuang, Jianmin Jiang, and Guoqiang Xiao. "SVM-Based Classification of Moving Objects." In Communications in Computer and Information Science, 37–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10512-8_5.

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Huerta, Ramón, Shankar Vembu, Mehmet K. Muezzinoglu, and Alexander Vergara. "Dynamical SVM for Time Series Classification." In Lecture Notes in Computer Science, 216–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32717-9_22.

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Conference papers on the topic "SVM classification"

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Mandava, Mani Swetha, Devika Jadhav, and Roshan Ramakrishna Naik. "Fault classification using SVM." In 2015 IEEE International Circuits and Systems Symposium (ICSyS). IEEE, 2015. http://dx.doi.org/10.1109/circuitsandsystems.2015.7394056.

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Cai, Hong, and Yufeng Wang. "Transcriptomic analysis using SVD clustering and SVM classification." In 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169476.

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Patil, B., S. Nandyal, and A. Pattanshetty. "Plant classification using SVM classifier." In Third International Conference on Computational Intelligence and Information Technology (CIIT 2013). Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/cp.2013.2639.

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Alzu’bi, Reem, A. Anushya, Ebtisam Hamed, Eng Abdelnour Al Sha’ar, and B. S. Angela Vincy. "Dates fruits classification using SVM." In INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, MATERIALS AND APPLIED SCIENCE. Author(s), 2018. http://dx.doi.org/10.1063/1.5032040.

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Dilrukshi, Inoshika, Kasun De Zoysa, and Amitha Caldera. "Twitter news classification using SVM." In 2013 8th International Conference on Computer Science & Education (ICCSE). IEEE, 2013. http://dx.doi.org/10.1109/iccse.2013.6553926.

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Kadir, Md Eusha, Pritom Saha Akash, Amin Ahsan Ali, Mohammad Shoyaib, and Zerina Begum. "Evidential SVM for binary classification." In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). IEEE, 2019. http://dx.doi.org/10.1109/icasert.2019.8934772.

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Este, Alice, Francesco Gringoli, and Luca Salgarelli. "On-line SVM traffic classification." In 2011 7th International Wireless Communications and Mobile Computing Conference (IWCMC 2011). IEEE, 2011. http://dx.doi.org/10.1109/iwcmc.2011.5982804.

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Niaf, Emilie, Remi Flamary, Carole Lartizien, and Stephane Canu. "Handling uncertainties in SVM classification." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967814.

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Frolov, Igor, and Rauf Sadykhov. "Pyramidal algorithm for SVM-classification." In 2012 IV International Conference "Problems of Cybernetics and Informatics" (PCI). IEEE, 2012. http://dx.doi.org/10.1109/icpci.2012.6486329.

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Patle, A., and D. S. Chouhan. "SVM kernel functions for classification." In 2013 International Conference on Advances in Technology and Engineering (ICATE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icadte.2013.6524743.

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Reports on the topic "SVM classification"

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Carin, Lawrence. ICA Feature Extraction and SVM Classification of FLIR Imagery. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada441506.

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Morris, Brendan, David W. Aha, Bryan Auslander, and Kalyan Gupta. Learning and Leveraging Context for Maritime Threat Analysis: Vessel Classification using Exemplar-SVM. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574666.

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