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Guermeur, Yann. "SVM Multiclasses, Théorie et Applications". Habilitation à diriger des recherches, Université Henri Poincaré - Nancy I, 2007. http://tel.archives-ouvertes.fr/tel-00203086.
Pełny tekst źródłaleurs performances constituent l'état de l'art dans de multiples domaines
de la reconnaissance des formes, d'autre part, elles possèdent des propriétés statistiques remarquables. Le premier modèle de SVM proposé par Vapnik et ses co-auteurs calcule des dichotomies. Il peut être utilisé pour effectuer des tâches de discrimination à catégories multiples, dans le cadre de l'application de méthodes de décomposition. Des SVM multi-classes ont également été proposées dans la littérature, parmi lesquelles nous distinguons celles qui s'appuient sur un modèle affine multivarié, que nous nommons M-SVM. Ce mémoire se présente comme une étude synthétique de la discrimination à catégories multiples au moyen de SVM. Il se concentre plus particulièrement sur l'analyse des M-SVM.
Le chapitre deux est consacré à la description des SVM multi-classes,
à leur mise en oeuvre et à l'analyse de leurs performances. Nous présentons successivement le cadre théorique de leur étude, les différents modèles, une étude théorique de leurs performances en généralisation, leur programmation ainsi que les différentes méthodes de sélection de modèle qui leur sont dédiées. Le chapitre trois décrit une application de la M-SVM de Weston et Watkins en biologie structurale prédictive. Le problème traité est la prédiction de la structure secondaire des protéines globulaires.
Krontorád, Jan. "Implementace algoritmu SVM v FPGA". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236773.
Pełny tekst źródłaSynek, Radovan. "Klasifikace textu pomocí metody SVM". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237229.
Pełny tekst źródłaMELONI, 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.
Pełny tekst źródłaClassificação 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.
Chen, Kathy F. (Kathy Fang-Yun). "Offline and online SVM performance analysis". Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41259.
Pełny tekst źródłaIncludes bibliographical references (p. 51-52).
To understand and evaluate the performance of a machine learning algorithm, the Support Vector Machine, this thesis compares the strengths and weaknesses between the offline and online SVM. The work includes the performance comparisons of SVMLight and LaSVM, with results of training time, number of support vectors, kernel evaluations, and test accuracies. Multiple datasets are experimented to cover a wide range of input data and training problems. Overall, the online LaSVM has trained with less time and returned comparable test accuracies than SVMLight. A general breakdown of the two algorithms and their computation efforts are included for detailed analysis.
by Kathy F. Chen.
M.Eng.
TEIXEIRA, JÚNIOR Talisman Cláudio de Queiroz. "Classificação fonética utilizando Boosting e SVM". Universidade Federal do Pará, 2006. http://repositorio.ufpa.br/jspui/2011/2533.
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Para compor um sistema de Reconhecimento Automático de Voz, pode ser utilizada uma tarefa chamada Classificação Fonética, onde a partir de uma amostra de voz decide-se qual fonema foi emitido por um interlocutor. Para facilitar a classificação e realçar as características mais marcantes dos fonemas, normalmente, as amostras de voz são pré- processadas através de um fronl-en'L Um fron:-end, geralmente, extrai um conjunto de parâmetros para cada amostra de voz. Após este processamento, estes parâmetros são insendos em um algoritmo classificador que (já devidamente treinado) procurará decidir qual o fonema emitido. Existe uma tendência de que quanto maior a quantidade de parâmetros utilizados no sistema, melhor será a taxa de acertos na classificação. A contrapartida para esta tendência é o maior custo computacional envolvido. A técnica de Seleção de Parâmetros tem como função mostrar quais os parâmetros mais relevantes (ou mais utilizados) em uma tarefa de classificação, possibilitando, assim, descobrir quais os parâmetros redundantes, que trazem pouca (ou nenhuma) contribuição à tarefa de classificação. A proposta deste trabalho é aplicar o classificador SVM à classificação fonética, utilizando a base de dados TIMIT, e descobrir os parâmetros mais relevantes na classificação, aplicando a técnica Boosting de Seleção de Parâmetros.
With the aim of setting up a Automatic Speech Recognition (ASR) system, a task named Phonetic Classification can be used. That task consists in, from a speech sample, deciding which phoneme was pronounced by a speaker. To ease the classification task and to enhance the most marked characteristics of the phonemes, the speech samples are usually pre-processed by a front-end. A front-end, as a general rule, extracts a set of features to each speech sample. After that, these features are inserted in a classification algorithm, that (already properly trained) will try to decide which phoneme was pronounced. There is a rule of thumb which says that the more features the system uses, the smaller the classification error rate will be. The disadvantage to that is the larger computational cost. Feature Selection task aims to show which are the most relevant (or more used) features in a classification task. Therefore, it is possible to discover which are the redundant features, that make little (or no) contribution to the classification task. The aim of this work is to apply SVM classificator in Phonetic Classification task, using TIMIT database, and discover the most relevant features in this classification using Boosting approach to implement Feature Selection.
Štechr, Vladislav. "Využití SVM v prostředí finančních trhů". Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2016. http://www.nusl.cz/ntk/nusl-241651.
Pełny tekst źródłaYao, Xiaojun. "Méthodes Non-linéaires (ANNs, SVMs) : applications à la Classification et à la Corrélation des Propriétés Physicochimiques et Biologiques". Paris 7, 2004. http://www.theses.fr/2004PA077182.
Pełny tekst źródłaSýkora, Michal. "Automatické označování obrázků". Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236453.
Pełny tekst źródłaxu, wei. "SVM-based algorithms for aligning ontologies using literature". Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15974.
Pełny tekst źródłaOntologies is one of the key techniques used in Semantic Web establishment. Nowadays,many ontologies have been developed and it is critical to understand the relationships between the terms of the ontologies, i.e. we need to align the ontologies.
This thesis deals with an approach for finding relationships between ontologies using literature by classifying documents related to terms in the ontologies.
In this project the general method from [1] is used, but in the classifier generation part, a brand new classifier based on SVMs algorithm is implemented by LPU and SVMlight. We evaluate our approach and compare it to previous approaches.
Xu, Wei. "SVM-based algorithms for aligning ontologies using literature". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15974.
Pełny tekst źródłaPedapati, Praveena, i Rama Vaishnavi Tannedi. "BRAIN TUMOUR DETECTION USING HOG BY SVM". Thesis, Blekinge Tekniska Högskola, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15905.
Pełny tekst źródłaHess, Eric. "Ramp Loss SVM with L1-Norm Regularizaion". VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3538.
Pełny tekst źródłaCheung, Chi-Wai. "Probabilistic rank aggregation for multiple SVM ranking /". View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20CHEUNG.
Pełny tekst źródłaMcLaren, Mitchell Leigh. "Improving automatic speaker verification using SVM techniques". Thesis, Queensland University of Technology, 2009. https://eprints.qut.edu.au/32063/1/Mitchell_McLaren_Thesis.pdf.
Pełny tekst źródłaViani, Federico. "SVM-based Strategies as applied to Electromagnetics". Doctoral thesis, Università degli studi di Trento, 2010. https://hdl.handle.net/11572/368063.
Pełny tekst źródłaViani, Federico. "SVM-based Strategies as applied to Electromagnetics". Doctoral thesis, University of Trento, 2010. http://eprints-phd.biblio.unitn.it/578/1/Ph.D.Thesis.VIANI-December.2010.pdf.
Pełny tekst źródłaAlmasiri, osamah A. "SKIN CANCER DETECTION USING SVM-BASED CLASSIFICATION AND PSO FOR SEGMENTATION". VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5489.
Pełny tekst źródłaGuan, Wei. "New support vector machine formulations and algorithms with application to biomedical data analysis". Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41126.
Pełny tekst źródłaMaretto, Danilo Althmann. "Comparação entre maquinas de vetores de suporte por minimos quadrados (LS-SVM) e metodos lineares para transferencia de calibração". [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/249303.
Pełny tekst źródłaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Quimica
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Resumo: Este trabalho teve como objetivo comparar os métodos lineares de calibração "mínimos quadrados parciais" (PLS) e "padronização direta por partes" (PDS) e um método não-linear "máquina de vetores de suporte por mínimos quadrados" (LS-SVM) na transferência de calibração para modelos de calibração multivariada onde se determinou porcentagem de etanol em cachaça a cinco temperaturas diferentes e para modelos onde se determinou a porcentagem de proteína e gordura em ração para cães em três diferentes granulometrias através de espectroscopia na região do infravermelho próximo. Foram preparadas 50 amostras de cachaça entre 20,86 e 46,48% v/v através de diluição com água Milli-Q ou adição de etanol P.A. (Merck) à cachaça original. A porcentagem alcoólica foi obtida através de um densímetro digital Anton Paar DMA 4500 e os espectros a 5 temperaturas diferentes (15, 20, 25, 30 e 35ºC) foram obtidos na faixa de 850 a 1050 nm em um equipamento Agilent 8453. Um total de 38 amostras de ração moídas foi fornecido pela empresa Nutron Alimentos Ltda a qual realizou testes padrão para determinação de porcentagem de proteína e gordura nas mesmas. As amostras foram então peneiradas, sendo divididas em 3 grupos com tamanhos de partícula diferentes. Os espectros foram obtidos para todos os grupos de partículas de todas as amostras na faixa de 1000 a 2400 nm em um equipamento Varian Cary 5G. Foram feitas quatro propostas diferentes para se fazer a transferência de calibração para cada uma das três aplicações (determinação do teor de etanol em cachaça, e do teor de proteína e gordura em ração). Na grande maioria delas o LS-SVM foi quem apresentou modelos mais bem ajustados
Abstract: The aim of this work was to compare the linear methods of calibration ¿Partial Least Squares¿ (PLS) and ¿Piece-wise Direct Standardization¿ (PDS) and a nonlinear method ¿Least-Squares Support Vector Machines¿ (LS-SVM) on calibration transfer to multivariate calibration models to the determination of alcoholic grade in cachaça in five different temperatures and to determination of protein and fat content in dog food in three different particule sizes by using near infrared spectroscopy. It has been prepared 50 cachaça samples between 20.86 and 46.48% v/v through dilution with Milli-Q water or adding etanol P.A.(Merck) to the original cachaça. The alcoholic grade has been obtained through a Anton Paar DMA 4500 digital densimeter and the spectra in five different temperatures (15, 20, 25, 30 and 35ºC) has been obtained between 850 and 1050 nm in a Agilent 8453 equipament. The 38 grinded dog food samples were supplied by Nutron Alimentos Ltda wich has realized the standard tests to determination of protein and fat mass porcentage in them. The samples have been bolted, been divided in three groups with different particle sizes. The spectra have been obtained to all the particle groups of all samples between 100 and 24000 nm in a Varian Cary 5G equipament. It has been done four different proposals to do the calibration transfer to each one of the three applications (etanol grade in cachaça, and protein and fat in dog food). In the most of them LS-SVM has gotten better adjusted models
Mestrado
Quimica Analitica
Mestre em Química
Hoefel, Guilherme. "Learning a two-stage SVM/CRF sequence classifier". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p1450397.
Pełny tekst źródłaTitle from first page of PDF file (viewed Apr. 28, 2008). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 30-31).
Jiang, Fuhua. "SVM-Based Negative Data Mining to Binary Classification". Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_diss/8.
Pełny tekst źródłaPanaganti, Shilpa. "Parallel SVM with Application to Protein Structure Prediction". Digital Archive @ GSU, 2004. http://digitalarchive.gsu.edu/cs_theses/3.
Pełny tekst źródłaŞimşek, Kadir Püskülcü Halis. "Categorization of Web Sites In Turkey With SVM/". [s.l.]: [s.n.], 2004. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000450.pdf.
Pełny tekst źródłaDiddikadi, Abhishek. "Multi Criteria Mapping Based on SVM and Clustering Methods". Master's thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-187132.
Pełny tekst źródłaFruci, Giuseppe. "Sviluppo e validazione di un algoritmo per la classificazione veglia-sonno". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amslaurea.unibo.it/6966/.
Pełny tekst źródłaPetruška, Ľubomír. "Simulace řídicích struktur elektromechanických systémů". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218310.
Pełny tekst źródłaCaruana, Godwin. "MapReduce based RDF assisted distributed SVM for high throughput spam filtering". Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/7572.
Pełny tekst źródłaTarasova, 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.
Pełny tekst źródłaThis 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
Lin, Min-Shea, i 林民勗. "Credit Card Verification and Recommendation System Using SVM and SVC". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/21606868084792725656.
Pełny tekst źródła國立高雄應用科技大學
資訊管理研究所碩士班
97
According to information from the Financial Supervisory Commission of Executive Yuan, there are 35,947,458 credit cards in 2008 Taiwan. In average, each person holds 4.1 credit cards. In this situation, many financial institutions distribute credit card too much, and it leads many problem. First, many ill-credible customers can applied for the credit card. This will increase credit risk and doubtful account infinancial institution. Second, many kinds of credit card had developed to vavious the demand of customers, but this also makes a lot of unused credit cards. That will not only waste the marketing cost but also lose customers. The goal of this thesis is to construct a system for financial institution for credit card verification and recommendation. The SVM (Support Vector Machine) is used to verify credit card and filter ill-credible customer, and The SVC (Support Vector Cluster) is used for credit card recommendation system.
Wang, Tsong-Yuh, i 王琮郁. "A Study on Analyzing Microarray Data using SVM and SOM". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/23899857310477290298.
Pełny tekst źródła國立清華大學
資訊系統與應用研究所
95
Researchers majoring in biology and medical science believe we could cure kinds of diseases if the genes which lead to the diseases are fixed. The development of microaray grew fast in recent years in order to make human genes readable. When we get a microarray image, the gene expression values can be computed by segmentation methods. After the gene expression is computed from the microarray image, the following important research is to analyze the gene expression. Because the range of the value of the gene expression is too huge for us to compute, we have to normalize the gene expression values first. Then we use smoothly clipped absolute deviation (SCAD) SVM and weighted punishment on overlap (WEPO) to screen the important genes. When these important genes (features) are found out, they are used in two classification methods, support vector machines (SVM) and self-organizing map (SOM). Finally, we can understand more properties of microarray data by the experimental results of gene selection and classification methods.
Chen, Chih-Ming, i 陳志明. "A Comparison of Texture Features Baesd On SVM and SOM". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/64662021927218084277.
Pełny tekst źródła國立清華大學
資訊工程學系
94
Experimental results of using various texture features based on Support Vector Machine (SVM) and Self-Organizing Map (SOM) are reported in this thesis. For classification, the texture features are derived from Gabor and four wavelet transforms (9/7, 5/3, Daubechies’ four, and Haar transforms). Then, the performance of various texture features will be evaluated by SVM and SOM. Moreover, a comparison of SVM and SOM for texture classification will be presented and illustrated in the final experimental results. Besides, the texture features for SVM and SOM have worked from a lightly different viewpoint; the training data with scaling or non-scaling process may heavily affect the classification rate. Our database consists of 96 classes as homogeneous as possible (1536 images of size 128×128) from Brodatz’s album. So the performance evaluation of various texture features with SVM and SOM will be tested on our database and be reported in the experimental results.
Liu, Jung, i 劉蓉. "Shapes-based Image Retrieval Using Fourier Descriptor by SVM and SOM". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/18325379807565019285.
Pełny tekst źródła中華大學
資訊管理學系
94
Content-based image retrieval has become an important topic in image processing and computer vision. In this paper, we used differ features such as moment invariant, centroid Fourier descriptor, and complex Fourier descriptor in Support Vector Machine for the shape-based image retrieval. The result shows that the best feature is the complex Fourier descriptor. Our approach is first to obtain the features. Secondly, the features and the class label are associated to form the training data and fed to the Support Vector Machine to get the training model. Then, the testing shapes are fed to the model to find the predicted class. Finally, we select the best matched image by computing the least mean square error distance among the images of the predicted class. We also demonstrate a way to track the pose of the testing image.
Tzeng, Zhong-Chiang, i 曾仲強. "Backdoor Detection based on SVM". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/11843859759127130083.
Pełny tekst źródła國立中山大學
資訊管理學系研究所
93
With the improvement of computer technologies and the wide use of the Internet, network security becomes more and more significant. According to the relevant statistics, malicious codes such as virus, worms, backdoors, and Trojans launch a lot of attacks. Backdoors are especially critical. Not only can it cross firewalls and antivirus software but also will steal confidential information and misuse network resources and launch attacks such as DDoS(Distributed Denial of Service). In this research, we analyze the properties and categories of backdoors and the application of data mining and support vector machines in intrusion detection. This research will focus on detecting the behavior of backdoor connection, and we propose a detecting architecture. The architecture is based on SVM, which is a machine learning method based on statistic theory and proposed by Vapnik to solve the problems in Neural Network techniques. In system modules, this research chooses IPAudit as our network monitor and libsvm as a SVM classifier. The packets captured by IPAudit will be classified into interactive or non-interactive flow by libsvm, and the result will be compared with legal service lists to determine whether a connection is a backdoor connection. We compare the accuracy of SVM, C4.5, and Na
Hsiao, Bo-wen, i 蕭博文. "SVM based counterfeit banknote recognition". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/37831926482654338436.
Pełny tekst źródła國立臺灣科技大學
電機工程系
97
With the advance in technology, the method of making counterfeit banknote is getting more and more sophisticated that leads to the failure of many banknotes detectors, such as banknote verification pen and ultraviolet tube that ordinary people and stores use. Therefore, the problem on how to detect counterfeit banknotes correctly has become an important issue. We will develop a new counterfeit banknote recognition system based on machine vision to alleviate the current technologies. In the early years, machine learning based banknote recognition systems are based on neural network, and researches on the description of features for banknotes have not been fully discussed. This study proposes an analysis method according to the texture features of NT banknotes in combination with simple backlight device, inclusive of color texture, grayscale texture and spatial structure features, and use PCA variable template matching algorithm to do banknote detection. Unlike NN which may converge to a local minimum, our method implements a real-time automatic optical counterfeit banknote recognition system which is machine vision based to increase the recognition rate and performance.
Fernandes, Miguel Ângelo Rodrigues. "Modelos Machine Learning e Modelos ARIMA na previsão do PSI20". Master's thesis, 2019. http://hdl.handle.net/10316/86372.
Pełny tekst źródłaEste trabalho tem como objetivo comparar o desempenho de modelos ARIMA e modelos machine learning, mais especificamente o modelo SVM, na previsão da variação do índice PSI20. Para o efeito, foi recolhido o preço de fecho do PSI20, 21 de Novembro de 2006 a 28 de Novembro de 2018 (dias úteis apenas) e procedeu-se à sua transformação em logaritmos. As previsões foram testadas numa subamostra correspondente a 30% da amostra total. Primeiramente, testou-se o modelo ARIMA. A especificação foi escolhida através dos critérios de informação AIC, BIC e HQC, não tendo os resultados sido consensuais quanto ao modelo ARIMA a utilizar. O teste AIC sugeriu o modelo ARIMA (3;0;3) e os testes BIC e HQC sugeriram o modelo ARIMA (0;0;1). Destes dois modelos, aquele que apresentou um menor root mean square error (RMSE) foi o modelo ARIMA (0;0;1). De seguida, testou-se o modelo SVM, tendo o software utilizado (Gretl) selecionado o modelo ε-SVR. Os restantes elementos da especificação foram escolhidos de acordo com o seu desempenho, concluindo-se que o melhor modelo SVM é um modelo ε-SVR que utiliza uma função de kernel do tipo linear. Este é também o melhor modelo entre todos os estudados, embora o RMSE varie muito pouco. Os modelos foram também usados para a previsão do sinal da variação da cotação do PSI20. Neste caso, os critérios de informação AIC, BIC e HQC foram concensuais quanto à especificação do modelo ARIMA, uma vez que todos os critérios apontam para a utilização do modelo ARIMA (0;0;1). O melhor modelo SVM com função de kernel linear permaneceu superior aos restantes modelos SVM, no entanto, o modelo ARIMA (0;0;1) conseguiu uma melhor taxa de acerto na previsão do sinal da variação da cotação do PSI20.
Abstract This work compares the performance of ARIMA and machine learning models, specifically the SVM model, in predicting the evolution of the PSI20. For this purpose, the PSI20 closing price was collected between 21 November 2006 and 28 November 2018 (working days only) and transformed using logarithms. Both predictions were tested in a subsample corresponding to 30%. Firstly, the ARIMA model was tested. The specification was chosen by means of the information criteria AIC, BIC and HQC. The information criteria did not select the same ARIMA model. The AIC test suggested the ARIMA (3; 0; 3) and the BIC and HQC suggested the ARIMA (0; 0; 1). Of these two models, the one with the lowest root mean square error (RMSE) is the ARIMA (0; 0; 1). Then, the SVM model was tested. The software used (Gretl) selected an ε-SVR model. The remaining elements of the model were chosen according to the out-of-sample performance. The best SVM model is an ε-SVR model that uses a linear type kernel function. This is also the best among all models considered, but note that the RMSE varies very little across specifications. The models were also used to predict the sign of the change of the PSI20. In this situation, the information criteria AIC, BIC and HQC were consensual regarding the specification of the arima model, since all criteria point to the use of ARIMA (0;0;1). The SVM model with linear kernel function remained the best of the SVM models, however, the ARIMA (0;0;1) model achieved a better prediction rate of the PSI20 price change signal.
Mukherjee, Sayan, i Vladimir Vapnik. "Multivariate Density Estimation: An SVM Approach". 1999. http://hdl.handle.net/1721.1/7260.
Pełny tekst źródłaChou, Wen-Hui, i 周文輝. "Classification of Hospitals : an SVM Approach". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/33810974606132578249.
Pełny tekst źródła國立東華大學
應用數學系
90
The financial condition of National Health Insurance Program has been deteriorating since its operation。Effective planning and management call for better understanding about the cost and spending patterns in the health care data。 However。 utilization of this data set is a challenging statistical task due to its complexity and size。In this study, we use Support Vector Machine (SVM) and logistic regression model to classify the hospitals. For most medium to large hospitals, we compare similarity among them using medical expense information。 Based on their training and testing errors against each other, we classify these hospitals into four categories。 Discussion on the appropriateness of this categorization and interpretation is also given。
Chiou, Wen-Izan, i 邱文贊. "Study on SVM-based Prediction Models". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/13611288854976014901.
Pełny tekst źródłaJhou, Wun-De, i 周文德. "Research of SVM-Based Speaker Identification". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/90362013324951747175.
Pełny tekst źródła國立暨南國際大學
電機工程學系
96
The thesis is investigated into training models of support vector machine (SVM) to compare with another ways, and used different methods of enhancement to improve the performance in the speaker identification system. In the study, we used Mel-frequency cepstral coefficients (MFCC) to convert the speaker data as the features of speaker identification. However, the noisy background in our life may interfere with the performance, such as noise on the streets, factories, and so on. The thesis will employ principal component analysis (PCA) and linear discriminant analysis (LDA) to enhance speaker features, then using SVM and Gaussian mixture model (GMM) to set up speaker models. Next, we used the system to identify the speakers. We adopted numbers in Chinese (0-9) from 20 speakers (10 males and 10 females), then everyone chanted 20 times for each number (total files: 4000). We selected 160 files of each one as the training file, the remainder as the testing files. Finally, we compared and discussed the results which are tested in several variable background noises form different conditions.
LI, JUN-HAN, i 李俊翰. "Credit Card Rating Using Fuzzy-SVM". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/63402572600011258871.
Pełny tekst źródła國立高雄應用科技大學
資訊管理研究所碩士班
101
Since 1991 the Ministry of finance liberalization of the credit card market, many banks gradually relaxed credit policies. The result is financial industry losses consecutive for two years and caused the dual-card debt in Taiwan. Credit crisis is not only in Taiwan, there has similar problem around the world. Although it occured at different time, but also shows potential problems with using credit card.The goal of this thesis is to construct a system for financial institution for credit card rating. The Fuzzy-SVM (Fuzzy Support Vector Machine) is used to verify credit card and filter ill-credible customer.
楊健炘. "Kernel-Based SVM: Theory and Application". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/96957635162943930250.
Pełny tekst źródła張耀允. "Construct SVM classification tree via clustering". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/42861587580627431633.
Pełny tekst źródłaVentura, Francisco Luís Amado Reis. "SVM Optimization for Epileptic Seizure Prediction". Master's thesis, 2011. http://hdl.handle.net/10316/99839.
Pełny tekst źródłaSome of the epileptic patients cannot be treated by drugs or surgery, fact that a ects the patient's daily life. The quality of life of these patients would be extremely improved by the existence of e ective seizure prediction algorithms. Epileptic seizures prediction can be achieved considering it as a classi cation problem. In order to predict the occurrence of an epileptic episode, an ap- proach using computational intelligence methods is currently under develop- ment, on behalf of the EPILEPSIAE project. Twenty-two univariate features were extracted from EEG (electroencephalogram). For a real-time prediction of the epileptic seizures, the number of inputs must be reduced in order to achieve a fast detection of the seizures, while maintaining the predictive power. In this thesis, Support Vector Machines (SVM) were optimized by three evolutionary approaches: The Elitist Non-dominated Sorting Genetic Algo- rithm (NSGA-II), the Particle Swarm Optimization (PSO) and S Metric Selection - Evolutionary Multi-Objective Algorithm (SMS-EMOA). The pa- rameters under optimization were the inputs, and Cost and Gamma of the SVM classi ers. Several tests were made, with di erent formulations, in order to reduce the complexity of the problem. The results show that using these algorithms it is possible to achieve low- complex predictors with appropriate prediction performance.
Pönisch, Jens. "Grundlagen von Support Vector Machines (SVM)". 2019. https://monarch.qucosa.de/id/qucosa%3A33662.
Pełny tekst źródłaJhou, Wun-De. "Research of SVM-Based Speaker Identification". 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0020-0907200812341500.
Pełny tekst źródłaSu, En-wei, i 蘇恩緯. "A SVM-based Motion Planning Method". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/58207500599998748851.
Pełny tekst źródła國立臺灣科技大學
資訊工程系
100
A standard motion planning problem tries to create feasible moves of robot in order to achieve the objectives, such as avoiding obstacles, delivering items, returning to the charging stations, etc. Often continuous motion of a robot is handled as paths in the high dimensional configuration space. Previously two types of planners, probabilistic roadmap (PRM) and rapidly-exploring random tree (RRT), are used to compute proper motion of a robot, where PRM is generally used as a multiquery planner, while RRT is considered as a single-query planner. This paper proposes a motion planning framework based on support vector machine(SVM) called SVMP. One main advantage of SVMP is it reduces the number of initial guesses of feasible robot poses, thus decreases the time in executing local planners in most roadmap-based methods in complex environments. By employing SVM techniques into the planner, SVMP takes the global obstacle distribution into account and generates a roadmap of robot motion that tends to be pushed away from obstacles. The effectiveness and efficiency of SVMP are compared with PRM and RRT through experiments in environments with different complexities.
Li, zeyou, i 李則佑. "Applying PSO-SVM For Channel Equalization". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/21964865425522112791.
Pełny tekst źródła國立宜蘭大學
電機工程學系碩士班
100
The support vector machine (SVM) is a powerful tool for solving problems with high dimensional, nonlinearly, and is of excellent performance in classification. In this study, we propose SVM as channel equalization. To reconstruct the signal that has the inter symbol interference (ISI) and white Gaussian noise which in high speed communications environments. The SVM parameters will affect the identification of the result. Therefore, we use particle swarm optimization (PSO) to find the suit parameters in SVM. To obtain the channel equalization model and reconstruct the signal. The PSO-SVM equalizer to realize the Bayesian equalizer solution can be achieved efficiently. The performance degradation was nearly 1dB at SNR increased.
Chiu, Cheng-Te, i 邱政德. "HOG and SVM based Motorcycle Detection". Thesis, 2017. http://ndltd.ncl.edu.tw/handle/26s975.
Pełny tekst źródła國立臺灣海洋大學
電機工程學系
105
This dissertation is a research about image-recognition system for locomotive riders. The fast economic growth encourages people to purchase more motor vehicles for the pursuit of their enormous convenience. Due to the limit of existing space, the traffic is getting into a worse situation. Though the administrative authorities have set up strict regulation, some speculators who have the lucky mentality still don’t follow the regulations as the law enforcement unit cannot do the monitoring all the times. As a result, traffic disorders cause accidents. Nowadays there have been many researches basing on visual analyzing, machine learning and object tracking, but most of them are applied for face recognition, pedestrian detection, license plate identification and bicycle and car detection. Though some researches make special efforts on locomotives, but only focus on license plate identification, movement detection and tracking for the purpose of lane change assistance, anti-crash and safety-reminding. However, this paper is emphatically point out the associated applications to ban the illegal violation of locomotive drivers. Furthermore, the result obtained from this research can be also applied to lock on particular locomotive and then narrow down the identification of license plate, movement tracking and the counting of volume of locomotive on roads. The methodology of this paper is based on OpenCV architecture and adopts HOG and SVM as vital elements for building up an identification model. HOG is a dense sampling descriptor, which is used to calculate the statistical value of gradient direction information of local target in image. The method is each pixel is subjected to a gradient change operation, and the local shape information (such as the human body edge contour) can be obtained by the gradient histogram operation of each block, and the performance efficiency can be improved by using the overlapping local contrast normalization and it also effectively reduce the impact from the light or shadow. HOG parameter settings will be described in following chapters. SVM is an algorithm that can be used for classification and regression. It can generate a pattern from the input training data and introduce a new instance according to this pattern. After inputting the vector data, the SVM can output one predictive classification labels. The integration of the above methods will achieve a remarkable effect of identification. The contribution of this paper is to establish a model of locomotive profile sample, providing a reference to follow-up developers and allowing them to search out the characteristics from the studied samples, to reduce the misunderstanding rate by establishing valid samples and to expand the scope of use. The result of this research has achieved to distinguish locomotives between cars on road and locomotives on road between pedestrians on sidewalk in different circumstances. The identification rate has reached more than 70% in day or night. The result of this research can also assist the law enforcement unit to conduct the motoring of locomotives on roads to deter the speculators from violating the traffic regulations. Meanwhile, it can also help to reduce the manpower and the associated labor cost, to benefit a good public transportation and to bring down the occurrence of accidents.
CHIEN, FU-CHIN, i 簡甫親. "SVM-Based Helmet Wearing Detection System". Thesis, 2018. http://ndltd.ncl.edu.tw/handle/pxv9a8.
Pełny tekst źródła中華大學
電機工程學系
106
This paper is mainly to design a low-cost, highly practical helmet wear system. When a character enters the screen, it will automatically start detecting whether the character has a hard hat for determining whether there is a suspicious person wearing a shelter in the building. The system is built on a desktop computer and uses Webcam as an image capture device to reduce cost and improve practicability. The adaptive Gaussian mixture model foreground extraction method enables the system to be executed in a complex background environment, using image processing and SVM technologies, to achieve image identification, the ultimate goal is to help protect personnel and reduce human negligence so that suspicious people can be detestable.