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Dissertations / Theses on the topic 'Support Vector Machine'

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Published: 4 June 2021

Last updated: 25 July 2025

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1

Cardamone, Dario. "Support Vector Machine a Machine Learning Algorithm." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017.

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Nella presente tesi di laurea viene preso in considerazione l’algoritmo di classificazione Support Vector Machine. Piu` in particolare si considera la sua formulazione come problema di ottimizazione Mixed Integer Program per la classificazione binaria super- visionata di un set di dati.

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McChesney, Charlie. "External Support Vector Machine Clustering." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/409.

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The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that are mislabeled and a large distance from the SVM hyperplane. The SVM is then iteratively rerun followed by more label swapping until no more progress can be made. After this process, a high percentage of the previously unknown class labels of the data set will be known. With sub-cl

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

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

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Zigic, Ljiljana. "Direct L2 Support Vector Machine." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4274.

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This dissertation introduces a novel model for solving the L2 support vector machine dubbed Direct L2 Support Vector Machine (DL2 SVM). DL2 SVM represents a new classification model that transforms the SVM's underlying quadratic programming problem into a system of linear equations with nonnegativity constraints. The devised system of linear equations has a symmetric positive definite matrix and a solution vector has to be nonnegative. Furthermore, this dissertation introduces a novel algorithm dubbed Non-Negative Iterative Single Data Algorithm (NN ISDA) which solves the underlying DL2 SVM's

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Park, Yongwon Baskiyar Sanjeev. "Dynamic task scheduling onto heterogeneous machines using Support Vector Machine." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Computer_Science_and_Software_Engineering/Thesis/Park_Yong_50.pdf.

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Tsang, Wai-Hung. "Scaling up support vector machines /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20TSANG.

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Perez, Daniel Antonio. "Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34858.

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Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applica

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Wen, Tong 1970. "Support Vector Machine algorithms : analysis and applications." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8404.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 2002.<br>Includes bibliographical references (p. 89-97).<br>Support Vector Machines (SVMs) have attracted recent attention as a learning technique to attack classification problems. The goal of my thesis work is to improve computational algorithms as well as the mathematical understanding of SVMs, so that they can be easily applied to real problems. SVMs solve classification problems by learning from training examples. From the geometry, it is easy to formulate the finding of SVM classifiers as a linearly constrained

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Liu, Yufeng. "Multicategory psi-learning and support vector machine." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1085424065.

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Thesis (Ph. D.)--Ohio State University, 2004.<br>Title from first page of PDF file. Document formatted into pages; contains x, 71 p.; also includes graphics Includes bibliographical references (p. 69-71). Available online via OhioLINK's ETD Center

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Merat, Sepehr. "Clustering Via Supervised Support Vector Machines." ScholarWorks@UNO, 2008. http://scholarworks.uno.edu/td/857.

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An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) then has its labels switched to the other class label. The SVM is then re-run on the data set (with partly re-labeled data). The repetition

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Karlbom, Hannes. "Hybrid Machine Translation : Choosing the best translation with Support Vector Machines." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-304257.

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In the field of machine translation there are various systems available which have different strengths and weaknesses. This thesis investigates the combination of two systems, a rule based one and a statistical one, to see if such a hybrid system can provide higher quality translations. The classification approach was taken, where a support vector machine is used to choose which sentences from each of the two systems result in the best translation. To label the sentences from the collected data a new method of simulated annealing was applied and compared to previously tried heuristics. The res

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12

Wu, Zhili. "Regularization methods for support vector machines." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/912.

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13

Möller, Manuel. "Spamerkennung mit Support Vector Machines." Thesis, Universitätsbibliothek Chemnitz, 2005. http://nbn-resolving.de/urn:nbn:de:swb:ch1-200500580.

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Diese Arbeit zeigt ausgehend von einer Darstellung der theoretischen Grundlagen automatischer Textklassifikation, dass die aus der Statistical Learning Theory stammenden Support Vector Machines geeignet sind, zu einer präziseren Erkennung unerwünschter E-Mail-Werbung beizutragen. In einer Testumgebung mit einem Corpus von 20 000 E-Mails wurden Testläufe verschiedene Parameter der Vorverarbeitung und der Support Vector Machine automatisch evaluiert und grafisch visualisiert. Aufbauend darauf wird eine Erweiterung für die Open-Source-Software SpamAssassin beschrieben, die die vorhandenen Klassif

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Zhang, Hang. "Distributed Support Vector Machine With Graphics Processing Units." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/991.

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Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it still costs O(n2) computation time. In our SVM implementation, we can do training with huge data sets in a distributed manner (by breaking the dataset into chunks, then using Message Passing Interface (MPI) to distribute each chunk to a different machine and processing SVM training within each chun

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Sandoval, Rodríguez Rodrigo Antonio. "Metodología de clasificación dinámica utilizando Support Vector Machine." Tesis, Universidad de Chile, 2007. http://www.repositorio.uchile.cl/handle/2250/102921.

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Esta investigación se centra en el problema de clasificación, por medio de confeccionar una metodología que permita detectar y modelar cambios en los patrones que definen la clasificación en el tiempo, en otras palabras, clasificación dinámica. La metodología desarrollada propone utilizar los resultados obtenidos en un periodo de tiempo para la construcción del modelo al siguiente periodo. Para ello se utilizaron dos modelos de clasificación distintos; el primero de ellos es Support Vector Machine (SVM) con el objetivo de confeccionar la metodología dinámica, que denominaremos Dynamic Suppo

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Weston, Jason Aaron Edward. "Extensions to the support vector method." Thesis, Royal Holloway, University of London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367838.

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Liu, Yi. "Studies on support vector machines and applications to video object extraction." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1158588434.

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Aleti, Kalyan Reddy. "E-quality control a support vector machines approach /." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Shilton, Alistair. "Design and training of support vector machines." Connect to thesis, 2006. http://repository.unimelb.edu.au/10187/443.

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In this thesis I introduce a new and novel form of SVM known as regression with inequalities, in addition to the standard SVM formulations of binary classification and regression. This extension encompasses both binary classification and regression, reducing the workload when extending the general form; and also provides theoretical insight into the underlying connections between the two formulations.

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Craddock, Richard Cameron. "Support vector classification analysis of resting state functional connectivity fMRI." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31774.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.<br>Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Saxena, Vishal 1979. "Support vector machine and its applications in information processing." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/29404.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.<br>Includes bibliographical references (leaves 59-61).<br>With increasing amounts of data being generated by businesses and researchers there is a need for fast, accurate and robust algorithms for data analysis. Improvements in databases technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. The primary aim of data mining is to discover patterns in the data that lead to better understanding of the data generating p

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Tuan, Nguyen Thanh, and 阮清俊. "Online Transductive Support Vector Machine." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/24865175611241572360.

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碩士<br>大葉大學<br>電機工程學系<br>96<br>The support vector machine (SVM) is one kind of machine learning methods based on the Statistical Learning Theory. The SVM is designed to construct a separating hyperplane between two classes of points, such that the margin between the hyperplane and the points closest to it becomes maximal. However, there are some disadvantages in applying the SVM in pattern classification problems. Firstly, the SVM is usually trained by supervised learning. Thus, the SVM model needs to be retrained from scratch whenever a new sample arrives. Secondly, the SVM trained with only a

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Chen, Tsai-Ying, and 陳采瀅. "The Compressed Smooth Support Vector Machine." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/45014192212713031489.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>105<br>In most cases, the real world data is not linearly separable. Under this condition, we can use the nonlinear Support Vector Machines (SVM), which involves nonlinear kernel matrix, instead of the linear SVM as a classification optimization problem. A key problem with that the disadvantages of the nonlinear SVM are apparent when encountering large-scale data in that the size of the kernel matrix grows quadratically with the size of the data which makes in-memory computation intractable. Our aim is to avoid the intractability of the nonlinear SVM on large-scale d

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Hung-Yuan, Tseng. "Face Recognition Using Support Vector Machine." 2005. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0002-1209200513495300.

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Tseng, Hung-Yuan, and 曾宏永. "Face Recognition Using Support Vector Machine." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/84749249050598684400.

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碩士<br>淡江大學<br>航空太空工程學系碩士班<br>93<br>Human face detection and recognition plays an important role in application such as video surveillance, personal security and face database management. A novel Support Vector Machines (SVM) is adopted for face recognition. SVM can handle classification problem effectively without establishing the prior knowledge database, and obtain support vector and related margin. To shorten the computing time, a modified version of SVM, namely Lagrangian support vector machine (LSVM) is applied here. An effective method to deal with the eyes and mouth region is pr

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Chien, Li-Jen, and 簡立仁. "Robust Smooth Support Vector Machine Learning." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/81221681928172170409.

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博士<br>國立臺灣科技大學<br>資訊工程系<br>98<br>This dissertation proposes four robust smooth support vector machine learning methodologies. First, we propose a new approach to generate representative reduced set for RSVM. Clustering reduced support vector machine (CRSVM) generates cluster centroids of each class and uses them to form the reduced set. By estimating the approximate density for each cluster, we can compute the width parameter used in Gaussian kernel. Secondly, we modify the previous 2-norm soft margin smooth support vector machine (SSVM2) to propose a new 1-norm soft margin smooth support vect

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Chen, Bo-Juen, and 陳博準. "Two applications of support vector machine." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/72678206990554657768.

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Lin, Xiao-Rong, and 林筱榮. "Text Detection Using Support Vector Machine." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/23636792602999015895.

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碩士<br>元智大學<br>資訊工程學系<br>96<br>Detecting text from image or video streams can be widely used in a variety of application fields. For example, detecting and extracting text from documents in advance can facilitate following OCR module. For automatic recognition of vehicle license plate, the key point is to locate the position of vehicle license plate successfully. For advertisement design, it is easy to replace text or background if text regions can be automatically extracted. For content-based retrieval, automatic extraction of text regions can lead more efficient and effective retrieval result

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Tang, Lung, and 唐龍. "Image Compression using Support Vector Machine." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/53037646816913726174.

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碩士<br>義守大學<br>資訊工程學系<br>91<br>Image compression addresses the problem of how to reduce the amount of data required to represent the image and the basic process is the removal of redundant data in the image. In the researches of image compression, there are many kinds of techniques such as vector quantization (VQ), Wavelet transform, fractal theory, well-known standard of compression --- JPEG, GIF and so on. Support vector machine (SVM) is a learning system, which was first introduced by Vapnik in 1992. There are two main categories for support vector machines: support vector classif

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Liao, Ping-Lun, and 廖柄. "Support Vector Machine for Smart Home." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/zkdgmm.

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碩士<br>國立虎尾科技大學<br>資訊工程研究所<br>98<br>Smart Home is based on the research of home digitization for human beings. The related researches report that context awareness is able to achieve the applications for a variety of scenarios and perform specific functions, which results in a more comfortable and convenient environment. In addition, the integration system can save energy and lower cost. Therefore, a model for integrating Bluetooth, Zigbee, Universal Serial Bus, WiFi standards is proposed. Those wireless technologies are used for gathering data. To simulate the scenarios of Smart Home, the prop

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Chen, Hsiang-Hsuan, and 陳祥瑄. "Distributed Consensus Reduced Support Vector Machine." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/as23xk.

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碩士<br>國立交通大學<br>應用數學系數學建模與科學計算碩士班<br>107<br>Nowadays, Machine learning performs astonishingly in many different fields. The more data we have, our machine learning methods show better results. However, in some cases, the data owners may not want to share the information they have, because those materials contain privacy issues. On the other hand, sometimes we encounter a very large dataset, which are difficult to store in a single machine. To deal with these two problems, we propose the distributed consensus reduced support vector machine (DCRSVM) for binary classification. Imagine that we hav

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Das, Anshuman. "Mining Machine Reliability Analysis Using Ensembled Support Vector Machine." Thesis, 2012. http://ethesis.nitrkl.ac.in/3324/1/Anshuman_Das%2C_108MN053.pdf.

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Estimation of reliability plays an important role in performance assessment of any system. Reliability predictions are important for various purposes, like production planning, maintenance planning, reliability assessment, fault detection in manufacturing processes, and risk and liability evaluation. In this study, a Support vector machine (SVM)-based model for forecasting reliability was developed. A genetic algorithm was applied for selecting SVM parameters. The developed model was validated by applying two benchmark data sets. A comparative study reveals that the proposed method performs be

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Huang, Zheng-Cheng, and 黃政誠. "Performance analysis of Spark Support Vector Machine." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/aq4ekc.

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碩士<br>國立雲林科技大學<br>資訊管理系<br>105<br>Due to the fast-paced nature of technology, the data of network world and real world increase enormously. Nowadays, the technology of computer has been unable gradually to load a huge amount of dataset to accommodate this enormous data, the concept of cloud computing and big data has been developed. The combination of both has brought a lot of commercial transactions. In recent years, the spark in memory through the proposed cluster computing concept, to solve many Hadoop general computation time long problem, because the use spark operation is performed in me

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Meng, Chao-Hong, and 孟昭宏. "Phone Recognition using Structural Support Vector Machine." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/45917295138142120792.

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Tsai, Ming-Chia, and 蔡明嘉. "Nasal event detection using support vector machine." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/06359129646759103280.

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Ying, Chiou Chiun, and 邱群穎. "Monitoring correlated processes using support vector machine." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/07927030419295046584.

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碩士<br>輔仁大學<br>管理學研究所<br>95<br>Traditional statistical process control (SPC) techniques are not applicable in many process industries due to the assumption of uncorrelated datasets. Several methods have been proposed to deal with autocorrelated parameters. In this research, we demonstrate that support vector machine (SVM) can be effective tools to identify shifts in process parameter values from AR(1) processes with various values of the autocorrelation coefficient. There are two kinds of monitoring models built in this thesis. One is 2 classifications model identifying if there is any s

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Lin, Chang-ching, and 林長青. "Support Vector Machine for Discovering Scientific Equations." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/80988014791877451365.

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碩士<br>國立雲林科技大學<br>電子與資訊工程研究所碩士班<br>91<br>Support vector machine (SVM) is a popular method of machine learning. Support vector machine has been applied to biotechnology, text categorization and image recognition. Support vector machine has good performance like decision tree and artificial neural network. In this thesis we use SVM to rediscover scientific equations from given scientific data. Finally we compare BACON’s method on discovering equations to support vector machine.

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Liu, Kun-Xain, and 劉堃憲. "Metagenomic Phylogenetic Classification with Support Vector Machine." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/81513256489784510377.

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碩士<br>逢甲大學<br>資訊工程學系<br>103<br>Metagenomics in environmental samples collected each DNA is unidentified DNA may have more sample in environmental. Metagenomics face a major challenge is to analyze the binning of the original sequence to be the same or similar communities in the category. The new sequencing technology makes it easier for metagenomics, simple and quick to get the sequence, but also more difficult to classify, because of short sequences to produce more than the previous technology. Classification short sequence 100 base pairs (bp) have until now relatively inaccurate, researchers

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Huang, Chien-Ming, and 黃建銘. "Smile Detection based on Support Vector Machine." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/72875756469700143588.

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碩士<br>南台科技大學<br>資訊工程系<br>97<br>The main purpose of this study is to present a method which applies Support Vector Machines which is maintained by two color spaces to Face Detection. First of all, the researcher transferred an image from RGB color space to YCbCr and Normalized Color Coordinates (NCC) color space to reduce the influence which is affected by different light condition to skin sample. After color space translation, the researcher could obtain YCbCr skin and NCC skin, and then found out some skin regions from original image by fuzzy computation. Finally, these skin regions were inp

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Huang, Bo-Jhen, and 黃柏禎. "Radar Precipitation Estimation Using Support Vector Machine." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/60276377877488607102.

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碩士<br>臺北市立大學<br>資訊科學系碩士在職專班<br>103<br>Significant problems exist for traditional precipitation estimates from radar when it comes to comparing with rain gauges on the ground. To avoid the problem and better utilize the information in the four-dimensional structure of the atmosphere, this research proposes a precipitation estimation method based on support vector machine and regression in the hopes to improve the accuracy of precipitation estimates. To generate various feature sets as inputs for the experiments, a set of tools have been developed, including a radar feature extraction and labeli

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Tsai, Yu Shiue, and 蔡豫學. "Reservoir Drought Prediction Using Support Vector Machine." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/32942032852924958649.

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碩士<br>國立屏東科技大學<br>水土保持系所<br>99<br>Drought often occurs without signs. When facing drought disasters, the best moment for making water restriction policy has already passed. The build of early warning system of drought can help better allocate limited water resource. In this study, five models (Support Vector Machine, SVM, Artificial Neural Network, ANN, multi-variables regression, maximum likelihood and Baysin classifier) were established to predict reservoir drought in next 1-9 ten-days in Tsengwen Reservoir.The discrimination drought prediction models use three ways; two class- drought condi

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Su, Yen-Ting, and 蘇彥庭. "TAIEX Trend Prediction with Support Vector Machine." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/61892041901017770533.

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碩士<br>國立中山大學<br>財務管理學系研究所<br>104<br>This paper develops a prediction model which combined genetic algorithms and support vector machine to predict the five-day-ahead direction of the TAIEX. We employ technical indicators as model input features, and the model output is upward or downward signal. To confirm the model output signal is able to make return from market, we use the output signals to do backtesting on TAIFEX between 2000 and 2014. In addition, we also try to use clustering and feature selection method to training data for improving the accuracy rate and investment return. The best pr

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Lin, Guan-Jhong, and 林冠中. "Sequential Support Vector Machine for Face Recognition." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/47391752650312695629.

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碩士<br>國立成功大學<br>資訊工程學系碩博士班<br>93<br>One effective approach is to adapt face models using new data in new environment. This thesis presents a novel sequential learning algorithm for the support vector machine（SVM） based face recognition system. First of all, we use a aussian probability model to represent the randomness of SVM parameters. The recursive Bayes theory is applied to sequentially update a posteriori distribution of SVM parameters. The estimated mean vector is adopted to build the output distribution of SVM. During test phase, we classify a test image according to output distribution

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Wang, Shiang-Ming, and 王祥銘. "Protein Crystallization Prediction Using Support Vector Machine." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/08623401823795019500.

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碩士<br>淡江大學<br>資訊工程學系碩士班<br>96<br>In structural genomics, proteins are essential materials that define life. A protein’s function is strongly related to it’s structure. The ultimate goal of structural research is to determine the three-dimensional structure of a protein. However, structure determination is often a time-consuming and expensive process. Also the process of experimental determination of protein structure has a high ratio of failures at different stages. There are two prevalent methods for protein structure determination - the magnetic resonance (NMR) spectroscopy and X-ray cryst

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Yang, Jyun-Siang, and 楊竣翔. "Human Face Identical using Support Vector Machine." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/5t5894.

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碩士<br>國立勤益科技大學<br>電子工程系<br>105<br>In this paper, the human face recognition is used for the purpose of research. The study takes two corners and the center point of eyes in the face, and two corners and the center point of the mouth as the feature points. The six feature points connected to each other are used as the eigenvector, and, finally, the face image recognition is completed by support vector machine technology combined with the eigenvector. In the course of the experiment, the face’s upper and lower parts respectively adapt skin color classification and texture technology, and use the

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Tsai, Liang-yu, and 蔡亮宇. "GPS Multipath Mitigation Using Support Vector Machine." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/j9e75t.

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碩士<br>國立臺灣海洋大學<br>通訊與導航工程學系<br>106<br>The positioning accuracy of GNSS is generally great in an open-sky environment. However, it usually exhibits poor positioning results in urban canyon environments due to pseudo-range measurement errors caused by multipath effect, which leads to performance degradation of the entire positioning system. For this reason, in order to maintain the accuracy of positioning systems, it is necessary to determine when the GNSS positioning is accurate and which satellites can have their pseudo-range measured accurately without multipath errors. Thus, the objective o

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Amayri, Ola. "On email spam filtering using support vector machine." Thesis, 2009. http://spectrum.library.concordia.ca/976212/1/MR63317.pdf.

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Electronic mail is a major revolution taking place over traditional communication systems due to its convenient, economical, fast, and easy to use nature. A major bottleneck in electronic communications is the enormous dissemination of unwanted, harmful emails known as "spam emails". A major concern is the developing of suitable filters that can adequately capture those emails and achieve high performance rate. Machine learning (ML) researchers have developed many approaches in order to tackle this problem. Within the context of machine learning, support vector machines (SVM) have made a large

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Chen, Po-Hao, and 陳伯豪. "Stock Indices Forecasting Using a Support Vector Machine." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/89862312651459498965.

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碩士<br>國立交通大學<br>科技管理研究所<br>92<br>This thesis deals with the application of a novel neural network technique, Support Vector Machine (SVM), in stock indices movement prediction. The purpose of this thesis is to demonstrate and verify the predictability of stock index direction using SVM, to develop effective trading strategies and to test the relative performance. A real future contract (Taiwan Stock Exchange Capitalization Weighted Stock Index) collected from Taiwan Futures Exchange is used as the data set. The series of relative difference in percentage of price (RDP) is adopted as the input

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Chang, Chih-Cheng, and 張志丞. "Smooth Support Vector Machine for Multi-class Classification." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/n7sreh.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>95<br>The smooth technique has been introduced into SVMs for both binary classification and regression problems. In this work, we extend the previous work in Smooth Support Vector Machine (SSVM) from binary to k-class classification based on a single machine approach and call it Multi-class Smooth Support Vector Machine (MSSVM). Similar to other single machine approaches, MSSVM will become very complicated and very difficult to solve when k is large. We only implement MSSVM for trinary classification problems. For k>3, we propose a Smooth One-vs.-One-vs.-Rest (SOOR)

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黃泓偉. "To identify nonobjective images by Support Vector Machine." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/tu9d2j.

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碩士<br>國立高雄應用科技大學<br>資訊工程系<br>105<br>One of the difficulties in searching natural images is the semantic gap between low-level pixel data and the content that the image is perceived by human. Most of the research focus on entity images which often contain objects in it, such as dinosaurs, flowers and buses. On the contrary, to search for nonobjective images, for example, art, emotion, friendship, time and freeze, is a much more difficult challenge than typical entity images retrieval. Because nonobjective images can only be judged by the surrounding environment, feelings or subjective understan

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