Relevant bibliographies by topics / SVM classification

Contents

  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: 25 July 2025

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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
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Subha, R., and M. Pushpa Rani. "SVM based Iris Classification." International Journal of Computer Sciences and Engineering 6, no. 2 (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 (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 (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
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Deepthi, Medechal, Mosali Harini, Pandiri Sai Geethika, Vusirikala Kalyan, and K. Kishor. "Data Classification of Dark Web using SVM and S3VM." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 510–17. http://dx.doi.org/10.22214/ijraset.2023.55643.

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Abstract: There are many issues regarding the dark web structural Type. It also increases the number of cybercrimes like illegal trade, forums, Terrorist activity. By understanding online criminal’s actions are challenging because the data is available in a very great extent amount. In a recent day the Online crimes are increasing all over the world. The data related to different types of frauds and scams, such as phishing schemes, identity theft etc. The data and discussion related to the act of hacking (hacktivist) activities, this often involve political or social causes. In some parts of d
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MISS., NAMRATA N. RADE. "IMAGE TEXTURE CLASSIFICATION: SURF WITH SVM." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 7 (2017): 43–47. https://doi.org/10.5281/zenodo.1459102.

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<strong>Nowadays,various approaches of texture classification have been developed which works on acquired image features and separate them into different classes by using a specific classifier . This paper gives a state - of - the - art texture classification technique called Speeded up Robust Features (SURF) with SVM (Support Vector Machine) classifier. In this concept,image data representation is accomplished by capturing feature s in the form of key - points. SURF uses determinant of Hessian matrix to achieve point of interests on which description and classification is carried out. This me
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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 (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 (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
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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, traini
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Reynolds, Evan, Brian Callaghan, and Mousumi Banerjee. "SVM–CART for disease classification." Journal of Applied Statistics 46, no. 16 (2019): 2987–3007. http://dx.doi.org/10.1080/02664763.2019.1625876.

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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<br>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. Co
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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 prod
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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 d
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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, feat
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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&amp;T and Sprint. Huvudsyftet är att med hjälp av maskininlärningsalgoritmen Support Vector Machines (SVM) klassificera meddelanden i fyra kategorier - Hat, Orsak, Explicit och Övrigt - för att kunna identifiera ett hat-meddelande och dess orsak. Studien resulterade i två metoder: en "naiv" metod (the Naive Method, NM) och en mer "avancerad" metod (the Partial Timeline Method, PTM). NM är en binär metod i den bemärkelsen att den ställer frågan: "Tillhör denna tweet klassen Hat?". PTM ställer s
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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
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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
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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 th
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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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D, Conner Mark, and United States. National Aeronautics and Space Administration., eds. Identification and classification of transient signatures in over-land SSM/I imagery. National Aeronautics and Space Administration, 1994.

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

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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. Botanisch Museum en Herbarium van de Rijksuniversiteit te Utrecht], 1985.

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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. Prometeĭ, 2010.

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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. 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. 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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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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Brechner, Siegfried Arnold. Automatic coccolith classification and extraction of morphological features in SEM images. 2000.

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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. 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. 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. 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. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-48219-9_20.

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

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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. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_30.

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Behera, Sandhyalati, and Mihir Narayan Mohanty. "Classification of EEG Signal Using SVM." In Advances in Electrical Control and Signal Systems. 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. 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. 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. 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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Kiranagi, Soumya S., and Savitha Patil. "Medicinal Plant Classification Using SVM." In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON). IEEE, 2024. http://dx.doi.org/10.1109/nmitcon62075.2024.10699081.

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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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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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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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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. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada441506.

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Rodriguez, Ian. Hybrid SVM-KNN Model for Urban Air Quality Classification in Jakarta. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.l4t0ynwl.

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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. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada574666.

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Naseem, Shahid. Hand written digits classification and recognition using convolutional neural networks by implementing the techniques of MLP and SVM. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2303p8226220.

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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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Mishra, Vinod. Subspace Learning Machine (SLM): A New Approach to Classification and Regression. DEVCOM Army Research Laboratory, 2022. http://dx.doi.org/10.21236/ad1183920.

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Vigil, Steven. Synergistic Chem-Bio Detection/Classification Combining UV LIF and IR LIDAR (SV). Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1233599.

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Gonzalez Diez, Verónica M., John Redwood, and Lourdes Alvarez Prado. IDB-9: IDB Integrated Strategy for Climate Change Adaptation and Mitigation, and Sustainable and Renewable Energy. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0010531.

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This paper reviews the Inter-American Development Bank¿s Integrated Strategy for Climate Change Adaptation and Mitigation, and Sustainable and Renewable Energy (CCS). The Strategy was produced as a requirement of the IDB-9 Agreement and approved by Board of Executive Directors in March 2011. Management produced an Action Plan for the CCS in February 2012. Although the IDB-9 also mentions "food security" as a sector priority, the CCS does not include this topic, nor does it discuss "protection of the environment" more generally. In reviewing this and other sector strategies mandated in IDB-9, t
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