Dissertations / Theses on the topic 'Support vector machines'
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Meyer, David, Friedrich Leisch, and Kurt Hornik. "Benchmarking Support Vector Machines." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2002. http://epub.wu.ac.at/1578/1/document.pdf.
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Support Vector Machines (SVMs) are rarely benchmarked against other classification or regression methods. We compare a popular SVM implementation (libsvm) to 16 classification methods and 9 regression methods-all accessible through the software R-by the means of standard performance measures (classification error and mean squared error) which are also analyzed by the means of bias-variance decompositions. SVMs showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive.Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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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 Klassifikationsmechanismen um eine Klassifikation per Support Vector Machine erweitert.
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Gao, Yunchuan. "Multi-category support vector machines." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2006. http://proquest.umi.com/login?COPT=REJTPTU0NWQmSU5UPTAmVkVSPTI=&clientId=3739.
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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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Karatzoglou, Alexandros, David Meyer, and Kurt Hornik. "Support Vector Machines in R." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1500/1/document.pdf.
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Being among the most popular and efficient classification and regression methods currently available, implementations of support vector machines exist in almost every popular programming language. Currently four R packages contain SVM related software. The purpose of this paper is to present and compare these implementations. (author's abstract)Series: Research Report Series / Department of Statistics and Mathematics
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Hornik, Kurt, David Meyer, and Alexandros Karatzoglou. "Support Vector Machines in R." American Statistical Association, 2006. http://epub.wu.ac.at/3986/1/supportvector.pdf.
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Being among the most popular and efficient classification and regression methods
currently available, implementations of support vector machines exist in almost every
popular programming language. Currently four R packages contain SVM related software.
The purpose of this paper is to present and compare these implementations. (authors' abstract)
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Möller, Manuel. "Spamerkennung mit Support Vector Machines." [S.l. : s.n.], 2005. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB12046020.
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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 of the above process improves the separability until there is no misclassification. Variations on this type of clustering approach are shown.
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Youn, Eun Seog. "Feature selection in support vector machines." [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE1000171.
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Thesis (M.S.)--University of Florida, 2002.Title from title page of source document. Document formatted into pages; contains x, 50 p.; also contains graphics. Includes vita. Includes bibliographical references.
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Ganapathiraju, Aravind. "Support Vector Machines for Speech Recognition." MSSTATE, 2002. http://sun.library.msstate.edu/ETD-db/theses/available/etd-02202002-111027/.
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Hidden Markov models (HMM) with Gaussian mixture observation densities are the dominant approach in speech recognition. These systems typically use a representational model for acoustic modeling which can often be prone to overfitting and does not translate to improved discrimination. We propose a new paradigm centered on principles of structural risk minimization using a discriminative framework for speech recognition based on support vector machines (SVMs). SVMs have the ability to simultaneously optimize the representational and discriminative ability of the acoustic classifiers. We have developed the first SVM-based large vocabulary speech recognition system that improves performance over traditional HMM-based systems. This hybrid system achieves a state-of-the-art word error rate of 10.6% on a continuous alphadigit task ? a 10% improvement relative to an HMM system. On SWITCHBOARD, a large vocabulary task, the system improves performance over a traditional HMM system from 41.6% word error rate to 40.6%. This dissertation discusses several practical issues that arise when SVMs are incorporated into the hybrid system.
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Gruber, Fred. "EVOLUTIONARY OPTIMIZATION OF SUPPORT VECTOR MACHINES." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3092.
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Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This thesis deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents individual models for electronic commerce.M.S.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems
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Wan, Vincent. "Speaker verification using support vector machines." Thesis, University of Sheffield, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398619.
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Osuna, Edgar E. (Edgar Elias) 1970. "Support vector machines : training and applications." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9925.
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McWhorter, Samuel P. "Fundamental Issues in Support Vector Machines." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc500155/.
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This dissertation considers certain issues in support vector machines (SVMs), including a description of their construction, aspects of certain exponential kernels used in some SVMs, and a presentation of an algorithm that computes the necessary elements of their operation with proof of convergence. In its first section, this dissertation provides a reasonably complete description of SVMs and their theoretical basis, along with a few motivating examples and counterexamples. This section may be used as an accessible, stand-alone introduction to the subject of SVMs for the advanced undergraduate. Its second section provides a proof of the positive-definiteness of a certain useful function here called E and dened as follows: Let V be a complex inner product space. Let N be a function that maps a vector from V to its norm. Let p be a real number between 0 and 2 inclusive and for any in V , let ( be N() raised to the p-th power. Finally, let a be a positive real number. Then E() is exp(()). Although the result is not new (other proofs are known but involve deep properties of stochastic processes) this proof is accessible to advanced undergraduates with a decent grasp of linear algebra. Its final section presents an algorithm by Dr. Kallman (preprint), based on earlier Russian work by B.F. Mitchell, V.F Demyanov, and V.N. Malozemov, and proves its convergence. The section also discusses briefly architectural features of the algorithm expected to result in practical speed increases.
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Wu, Zhili. "Regularization methods for support vector machines." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/912.
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Trotter, M. W. B. "Support vector machines for drug discovery." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1445885/.
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Support vector machines (SVMs) have displayed good predictive accuracy on a wide range of classification tasks and are inherently adaptable to complex problem domains. Structure-property correlation (SPC) analysis is a vital part of the contemporary drug discovery process, in which several components of the search for novel molecular compounds with therapeutic potential may be performed by computer (in silicd). Inferred relationships between molecular structure and biological properties of interest are used to eliminate compounds unsuitable for further development. In order to improve process efficiency without rejecting useful compounds, predictive accuracy of such relationships must remain high despite a paucity of data from which to infer them. This thesis describes the application of SVMs to SPC analysis and investigates methods with which to enhance performance and facilitate integration of the technique into present practice. Overviews of contemporary drug discovery and the role of machine learning place the investigation into context. Computational discrimination between compounds according to their structures and properties of interest is described in detail, as is the SVM algorithm. A framework for the assessment of supervised machine learning performance on SPC data is proposed and employed to assess SVM performance alongside state-of-the-art techniques for in silico SPC analysis on data provided by GlaxoSmithKline. SVM performance is competitive and the comparison prompts adaptations of both data treatment and algorithmic application to explore the effects of data paucity, class imbalance and outlying data. Subsequent work weights the SVM kernel matrix to recognise heavily populated regions of training data and suggests the incorporation of domain-specific clustering methods to assist the standard SVM algorithm. The notion that SVM kernel functions may incorporate existing domain-specific methods leads to kernel functions that employ existing pharmaceutical similarity measures to treat an abstract, binary representation of molecular structure that is not used widely for SPC analysis.
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Wu, Jinran. "Statistical support vector machines with optimizations." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/234509/1/Jinran_Wu_Thesis.pdf.
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This thesis combines support vector machines with statistical models for analyzing data generated by complex processes. The key contribution of the thesis is to propose five regression frameworks aiming for hyperparameter estimation, support vector selection, data modelling with unequal variances, temporal patterns, and cost benefit analysis. A new optimizer is also proposed for high-dimensional optimization.
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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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MICKELIN, JOEL. "Named Entity Recognition with Support Vector Machines." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138012.
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This report describes a degree project in Computer Science, the aim of which was to construct a system for Named Entity Recognition in Swedish texts of names of people, locations and organizations, as well as expressions for time. This system was constructed from the part-of-speech tagger Granska and the Support Vector Machine system SVMlin. The completed system was trained to recognize Named Entities by analyzing patterns in training corpora consisting of lists of example words belonging to each category. The system was initially trained to recognize patterns based on individual characters in words, but was later rewritten to recognize other characteristics of individual words such as the types of characters the words contained. When evaluating the system, it was determined that no incarnation of the system managed to perform satisfactorily when tested to recognize Named Entities of the aforementioned categories. A possible reason for this is that three of the categories, i.e. names of people, names of locations and names of organizations have few or no distinguishing features between them, which might warrant more research. The system proved apt when tested with solving the related problem of distinguishing email addresses from other named entities, indicating that the system might be of use in some cases of Named Entity Recognition.Denna rapport beskriver ett examensarbete inom datalogi, målet med vilket var att konstruera ett system för igenkänning i svensk text av Named Entities för personnamn, platsnamn och namn på organisationer, samt tidsangivelser. Systemet konstruerades utgående från part-of-speech-taggaren Granska samt supportvektormaskinsystemet SVMlin. Det färdiga systemet tränades att känna igen Named Entities genom att analysera mönster i träningscorpora bestående av listor på exempelord tillhörande varje kategori. Systemet tränades först att känna igen mönster baserade på enskilda tecken i ord, men skrevs sedan om för att känna igen andra karakteristika hos enskilda ord såsom vilka slags tecken de innehåller. När systemet evaluerades framkom att ingen version av det fungerade tillfredsställande när det testades att känna igen Named Entities av ovan nämnda kategorier. En möjlig orsak till detta kan vara att tre av kategorierna, personnamn, platsnamn och namn på organisationer har få eller inga inneboende skillnader sinsemellan, vilket kan bli grund till mer forskning. Systemet visade sig dugligt när det prövades att lösa det relaterade problemet att särskilja mailadresser från andra Named Entities, vilket kan tyda på att systemet kan användas för viss typ av igenkänning av Named Entities.
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Ekambaram, Rajmadhan. "Label Noise Cleaning Using Support Vector Machines." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/5943.
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Mislabeled examples affect the performance of supervised learning algorithms. Two novel approaches to this problem are presented in this Thesis. Both methods build on the hypothesis that the large margin and the soft margin principles of support vector machines provide the characteristics to select mislabeled examples. Extensive experimental results on several datasets support this hypothesis. The support vectors of the one-class and two-class SVM classifiers captures around 85% and 99% of the randomly generated label noise examples (10% of the training data) on two character recognition datasets. The numbers of examples that need to be reviewed can be reduced by creating a two-class SVM classifier with the non-support vector examples, and then by only reviewing the support vector examples based on their classification score from the classifier. Experimental results on four datasets show that this method removes around 95% of the mislabeled examples by reviewing only around about 14% of the training data. The parameter independence of this method is also verified through the experiments. All the experimental results show that most of the label noise examples can be removed by (re-)examining the selective support vector examples. This property can be very useful while building large labeled datasets.
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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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Shah, Rohan Shiloh. "Support vector machines for classification and regression." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=100247.
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In the last decade Support Vector Machines (SVMs) have emerged as an important learning technique for solving classification and regression problems in various fields, most notably in computational biology, finance and text categorization. This is due in part to built-in mechanisms to ensure good generalization which leads to accurate prediction, the use of kernel functions to model non-linear distributions, the ability to train relatively quickly on large data sets using novel mathematical optimization techniques and most significantly the possibility of theoretical analysis using computational learning theory. In this thesis, we discuss the theoretical basis and computational approaches to Support Vector Machines.
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Brunner, Carl. "Pairwise Classification and Pairwise Support Vector Machines." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-87820.
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Several modifications have been suggested to extend binary classifiers to multiclass classification, for instance the One Against All technique, the One Against One technique, or Directed Acyclic Graphs. A recent approach for multiclass classification is the pairwise classification, which relies on two input examples instead of one and predicts whether the two input examples belong to the same class or to different classes. A Support Vector Machine (SVM), which is able to handle pairwise classification tasks, is called pairwise SVM. A common pairwise classification task is face recognition. In this area, a set of images is given for training and another set of images is given for testing. Often, one is interested in the interclass setting. The latter means that any person which is represented by an image in the training set is not represented by any image in the test set. From the mentioned multiclass classification techniques only the pairwise classification technique provides meaningful results in the interclass setting. For a pairwise classifier the order of the two examples should not influence the classification result. A common approach to enforce this symmetry is the use of selected kernels. Relations between such kernels and certain projections are provided. It is shown, that those projections can lead to an information loss. For pairwise SVMs another approach for enforcing symmetry is the symmetrization of the training sets. In other words, if the pair (a,b) of examples is a training pair then (b,a) is a training pair, too. It is proven that both approaches do lead to the same decision function for selected parameters. Empirical tests show that the approach using selected kernels is three to four times faster. For a good interclass generalization of pairwise SVMs training sets with several million training pairs are needed. A technique is presented which further speeds up the training time of pairwise SVMs by a factor of up to 130 and thus enables the learning of training sets with several million pairs. Another element affecting time is the need to select several parameters. Even with the applied speed up techniques a grid search over the set of parameters would be very expensive. Therefore, a model selection technique is introduced that is much less computationally expensive. In machine learning, the training set and the test set are created by using some data generating process. Several pairwise data generating processes are derived from a given non pairwise data generating process. Advantages and disadvantages of the different pairwise data generating processes are evaluated. Pairwise Bayes' Classifiers are introduced and their properties are discussed. It is shown that pairwise Bayes' Classifiers for interclass generalization tasks can differ from pairwise Bayes' Classifiers for interexample generalization tasks. In face recognition the interexample task implies that each person which is represented by an image in the test set is also represented by at least one image in the training set. Moreover, the set of images of the training set and the set of images of the test set are disjoint. Pairwise SVMs are applied to four synthetic and to two real world datasets. One of the real world datasets is the Labeled Faces in the Wild (LFW) database while the other one is provided by Cognitec Systems GmbH. Empirical evidence for the presented model selection heuristic, the discussion about the loss of information and the provided speed up techniques is given by the synthetic databases and it is shown that classifiers of pairwise SVMs lead to a similar quality as pairwise Bayes' classifiers. Additionally, a pairwise classifier is identified for the LFW database which leads to an average equal error rate (EER) of 0.0947 with a standard error of the mean (SEM) of 0.0057. This result is better than the result of the current state of the art classifier, namely the combined probabilistic linear discriminant analysis classifier, which leads to an average EER of 0.0993 and a SEM of 0.0051Es gibt verschiedene Ansätze, um binäre Klassifikatoren zur Mehrklassenklassifikation zu nutzen, zum Beispiel die One Against All Technik, die One Against One Technik oder Directed Acyclic Graphs. Paarweise Klassifikation ist ein neuerer Ansatz zur Mehrklassenklassifikation. Dieser Ansatz basiert auf der Verwendung von zwei Input Examples anstelle von einem und bestimmt, ob diese beiden Examples zur gleichen Klasse oder zu unterschiedlichen Klassen gehören. Eine Support Vector Machine (SVM), die für paarweise Klassifikationsaufgaben genutzt wird, heißt paarweise SVM. Beispielsweise werden Probleme der Gesichtserkennung als paarweise Klassifikationsaufgabe gestellt. Dazu nutzt man eine Menge von Bildern zum Training und ein andere Menge von Bildern zum Testen. Häufig ist man dabei an der Interclass Generalization interessiert. Das bedeutet, dass jede Person, die auf wenigstens einem Bild der Trainingsmenge dargestellt ist, auf keinem Bild der Testmenge vorkommt. Von allen erwähnten Mehrklassenklassifikationstechniken liefert nur die paarweise Klassifikationstechnik sinnvolle Ergebnisse für die Interclass Generalization. Die Entscheidung eines paarweisen Klassifikators sollte nicht von der Reihenfolge der zwei Input Examples abhängen. Diese Symmetrie wird häufig durch die Verwendung spezieller Kerne gesichert. Es werden Beziehungen zwischen solchen Kernen und bestimmten Projektionen hergeleitet. Zudem wird gezeigt, dass diese Projektionen zu einem Informationsverlust führen können. Für paarweise SVMs ist die Symmetrisierung der Trainingsmengen ein weiter Ansatz zur Sicherung der Symmetrie. Das bedeutet, wenn das Paar (a,b) von Input Examples zur Trainingsmenge gehört, dann muss das Paar (b,a) ebenfalls zur Trainingsmenge gehören. Es wird bewiesen, dass für bestimmte Parameter beide Ansätze zur gleichen Entscheidungsfunktion führen. Empirische Messungen zeigen, dass der Ansatz mittels spezieller Kerne drei bis viermal schneller ist. Um eine gute Interclass Generalization zu erreichen, werden bei paarweisen SVMs Trainingsmengen mit mehreren Millionen Paaren benötigt. Es wird eine Technik eingeführt, die die Trainingszeit von paarweisen SVMs um bis zum 130-fachen beschleunigt und es somit ermöglicht, Trainingsmengen mit mehreren Millionen Paaren zu verwenden. Auch die Auswahl guter Parameter für paarweise SVMs ist im Allgemeinen sehr zeitaufwendig. Selbst mit den beschriebenen Beschleunigungen ist eine Gittersuche in der Menge der Parameter sehr teuer. Daher wird eine Model Selection Technik eingeführt, die deutlich geringeren Aufwand erfordert. Im maschinellen Lernen werden die Trainingsmenge und die Testmenge von einem Datengenerierungsprozess erzeugt. Ausgehend von einem nicht paarweisen Datengenerierungsprozess werden unterschiedliche paarweise Datengenerierungsprozesse abgeleitet und ihre Vor- und Nachteile bewertet. Es werden paarweise Bayes-Klassifikatoren eingeführt und ihre Eigenschaften diskutiert. Es wird gezeigt, dass sich diese Bayes-Klassifikatoren für Interclass Generalization Aufgaben und für Interexample Generalization Aufgaben im Allgemeinen unterscheiden. Bei der Gesichtserkennung bedeutet die Interexample Generalization, dass jede Person, die auf einem Bild der Testmenge dargestellt ist, auch auf mindestens einem Bild der Trainingsmenge vorkommt. Außerdem ist der Durchschnitt der Menge der Bilder der Trainingsmenge mit der Menge der Bilder der Testmenge leer. Paarweise SVMs werden an vier synthetischen und an zwei Real World Datenbanken getestet. Eine der verwendeten Real World Datenbanken ist die Labeled Faces in the Wild (LFW) Datenbank. Die andere wurde von Cognitec Systems GmbH bereitgestellt. Die Annahmen der Model Selection Technik, die Diskussion über den Informationsverlust, sowie die präsentierten Beschleunigungstechniken werden durch empirische Messungen mit den synthetischen Datenbanken belegt. Zudem wird mittels dieser Datenbanken gezeigt, dass Klassifikatoren von paarweisen SVMs zu ähnlich guten Ergebnissen wie paarweise Bayes-Klassifikatoren führen. Für die LFW Datenbank wird ein paarweiser Klassifikator bestimmt, der zu einer durchschnittlichen Equal Error Rate (EER) von 0.0947 und einem Standard Error of The Mean (SEM) von 0.0057 führt. Dieses Ergebnis ist besser als das des aktuellen State of the Art Klassifikators, dem Combined Probabilistic Linear Discriminant Analysis Klassifikator. Dieser führt zu einer durchschnittlichen EER von 0.0993 und einem SEM von 0.0051
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Tavara, Shirin. "High-Performance Computing For Support Vector Machines." Licentiate thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-16556.
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Machine learning algorithms are very successful in solving classification and regression problems, however the immense amount of data created by digitalization slows down the training and predicting processes, if solvable at all. High-Performance Computing(HPC) and particularly parallel computing are promising tools for improving the performance of machine learning algorithms in terms of time. Support Vector Machines(SVM) is one of the most popular supervised machine learning techniques that enjoy the advancement of HPC to overcome the problems regarding big data, however, efficient parallel implementations of SVM is a complex endeavour. While there are many parallel techniques to facilitate the performance of SVM, there is no clear roadmap for every application scenario. This thesis is based on a collection of publications. It addresses the problems regarding parallel implementations of SVM through four research questions, all of which are answered through three research articles. In the first research question, the thesis investigates important factors such as parallel algorithms, HPC tools, and heuristics on the efficiency of parallel SVM implementation. This leads to identifying the state of the art parallel implementations of SVMs, their pros and cons, and suggests possible avenues for future research. It is up to the user to create a balance between the computation time and the classification accuracy. In the second research question, the thesis explores the impact of changes in problem size, and the value of corresponding SVM parameters that lead to significant performance. This leads to addressing the impact of the problem size on the optimal choice of important parameters. Besides, the thesis shows the existence of a threshold between the number of cores and the training time. In the third research question, the thesis investigates the impact of the network topology on the performance of a network-based SVM. This leads to three key contributions. The first contribution is to show how much the expansion property of the network impact the convergence. The next is to show which network topology is preferable to efficiently use the computing powers. Third is to supply an implementation making the theoretical advances practically available. The results show that graphs with large spectral gaps and higher degrees exhibit accelerated convergence. In the last research question, the thesis combines all contributions in the articles and offers recommendations towards implementing an efficient framework for SVMs regarding large-scale problems.
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Zhang, Shi-Xiong. "Structured support vector machines for speech recognition." Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708040.
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Kroon, Rodney Stephen. "Support vector machines, generalization bounds, and transduction." Thesis, Stellenbosch : University of Stellenbosch, 2003. http://hdl.handle.net/10019.1/16375.
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Kohram, Mojtaba. "Experiments with Support Vector Machines and Kernels." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378112059.
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Schulze, Martin Michael. "Facial expression recognition with support vector machines." [S.l. : s.n.], 2003. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10952963.
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Lin, Hsuan-Tien Abu-Mostafa Yaser S. "Infinite ensemble learning with Support Vector Machines /." Diss., Pasadena, Calif. : California Institute of Technology, 2005. http://resolver.caltech.edu/CaltechETD:etd-05262005-030549.
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Rogers, Spencer David. "Support Vector Machines for Classification and Imputation." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3215.
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Support vector machines (SVMs) are a powerful tool for classification problems. SVMs have only been developed in the last 20 years with the availability of cheap and abundant computing power. SVMs are a non-statistical approach and make no assumptions about the distribution of the data. Here support vector machines are applied to a classic data set from the machine learning literature and the out-of-sample misclassification rates are compared to other classification methods. Finally, an algorithm for using support vector machines to address the difficulty in imputing missing categorical data is proposed and its performance is demonstrated under three different scenarios using data from the 1997 National Labor Survey.
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Söyseth, Carl, and Gustav Ekelund. "Improving Support-vector machines with Hyperplane folding." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18305.
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Background. Hyperplane folding was introduced by Lars Lundberg et al. in Hyperplane folding increased the margin while suffering from a flaw, referred to asover-rotation in this thesis. The aim of this thesis is to introduce a new different technique thatwould not over-rotate data points. This novel technique is referred to as RubberBand folding in the thesis. The following research questions are addressed: 1) DoesRubber Band folding increases classification accuracy? 2) Does Rubber Band fold-ing increase the Margin? 3) How does Rubber Band folding effect execution time? Rubber Band folding was implemented and its result was compared toHyperplane folding and the Support-vector machine. This comparison was done byapplying Stratified ten-fold cross-validation on four data sets for research question1 & 2. Four folds were applied for both Hyperplane folding and Rubber Band fold-ing, as more folds can lead to over-fitting. While research question 3 used 15 folds,in order to see trends and is not affected by over-fitting. One BMI data set, wasartificially made for the initial Hyperplane folding paper. Another data set labeled patients with, or without a liver disorder. Another data set predicted if patients havebenign- or malign cancer cells. Finally, a data set predicted if a hepatitis patient isalive within five years.Results.Rubber Band folding achieved a higher classification accuracy when com-pared to Hyperplane folding in all data sets. Rubber Band folding increased theclassification in the BMI data set and cancer data set while the accuracy for Rub-ber Band folding decreased in liver and hepatitis data sets. Hyperplane folding’saccuracy decreased in all data sets.Both Rubber Band folding and Hyperplane folding increases the margin for alldata sets tested. Rubber Band folding achieved a margin higher than Hyperplanefolding’s in the BMI and Liver data sets. Execution time for both the classification ofdata points and the training time for the classifier increases linearly per fold. RubberBand folding has slower growth in classification time when compared to Hyperplanefolding. Rubber Band folding can increase the classification accuracy, in whichexact cases are unknown. It is howevered believed to be when the data is none-linearly seperable.Rubber Band folding increases the margin. When compared to Hyperplane fold-ing, Rubber Band folding can in some cases, achieve a higher increase in marginwhile in some cases Hyperplane folding achieves a higher margin.Both Hyperplane folding and Rubber Band folding increases training time andclassification time linearly. The difference between Hyperplane folding and RubberBand folding in training time was negligible while Rubber bands increase in classifi-cation time was lower. This was attributed to Rubber Band folding rotating fewerpoints after 15 folds.
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D'Orangeville, Vincent. "Analyse automatique de données par Support Vector Machines non supervisés." Thèse, Université de Sherbrooke, 2012. http://hdl.handle.net/11143/6678.
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Cette dissertation présente un ensemble d'algorithmes visant à en permettre un usage rapide, robuste et automatique des « Support Vector Machines » (SVM) non supervisés dans un contexte d'analyse de données. Les SVM non supervisés se déclinent sous deux types algorithmes prometteurs, le « Support Vector Clustering » (SVC) et le « Support Vector Domain Description » (SVDD), offrant respectivement une solution à deux problèmes importants en analyse de données, soit la recherche de groupements homogènes (« clustering »), ainsi que la reconnaissance d'éléments atypiques (« novelty/abnomaly detection ») à partir d'un ensemble de données. Cette recherche propose des solutions concrètes à trois limitations fondamentales inhérentes à ces deux algorithmes, notamment I) l'absence d'algorithme d'optimisation efficace permettant d'exécuter la phase d'entrainement des SVDD et SVC sur des ensembles de données volumineux dans un délai acceptable, 2) le manque d'efficacité et de robustesse des algorithmes existants de partitionnement des données pour SVC, ainsi que 3) l'absence de stratégies de sélection automatique des hyperparamètres pour SVDD et SVC contrôlant la complexité et la tolérance au bruit des modèles générés. La résolution individuelle des trois limitations mentionnées précédemment constitue les trois axes principaux de cette thèse doctorale, chacun faisant l'objet d'un article scientifique proposant des stratégies et algorithmes permettant un usage rapide, robuste et exempt de paramètres d'entrée des SVDD et SVC sur des ensembles de données arbitraires.
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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 results show that a hybrid system has an increased average BLEU score of 6.10% or 1.86 points over the single best system, and that using the labels created through simulated annealing, over heuristic rules, gives a significant improvement in classifier performance.
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Eryarsoy, Enes. "Using domain-specific knowledge in support vector machines." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011358.
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Eberts, Mona [Verfasser]. "Adaptive Rates for Support Vector Machines / Mona Eberts." Aachen : Shaker, 2015. http://d-nb.info/1070151874/34.
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Trinh, Xuan Tuan. "Online learning of multi-class Support Vector Machines." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-185084.
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Support Vector Machines (SVMs) are state-of-the-art learning algorithms forclassification problems due to their strong theoretical foundation and their goodperformance in practice. However, their extension from two-class to multi-classclassification problems is not straightforward. While some approaches solve a seriesof binary problems, other, theoretically more appealing methods, solve one singleoptimization problem. Training SVMs amounts to solving a convex quadraticoptimization problem. But even with a carefully tailored quadratic program solver,training all-in-one multi-class SVMs takes a long time for large scale datasets. We firstconsider the problem of training the multi-class SVM proposed by Lee, Lin and Wahba(LLW), which is the first Fisher consistent multi-class SVM that has been proposed inthe literature, and has recently been shown to exhibit good generalizationperformance on benchmark problems. Inspired by previous work on onlineoptimization of binary and multi-class SVMs, a fast approximative online solver for theLLW SVM is derived. It makes use of recent developments for efficiently solvingall-in-one multi-class SVMs without bias. In particular, it uses working sets of size twoinstead of following the paradigm of sequential minimal optimization. After successfulimplementation of the online LLW SVM solver, it is extended to also support thepopular multi-class SVM formulation by Crammer and Singer. This is done using arecently established unified framework for a larger class of all-in-one multi-class SVMs.This makes it very easy in the future to adapt the novel online solver to even moreformulations of multi-class SVMs. The results suggest that online solvers may providefor a robust and well-performing way of obtaining an approximative solution to thefull problem, such that it constitutes a good trade-off between optimization time andreaching a sufficiently high dual value.
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Fan, Jialun. "On Accelerating Training Procedure of Support Vector Machines." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518814.
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Joachims, Thorsten. "Learning to classify text using support vector machines /." Boston [u.a.] : Kluwer Acad. Publ, 2002. http://www.loc.gov/catdir/toc/fy032/2002022127.html.
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Falk, Jennie, and Gabriella Hultström. "Support Vector Machines for Optimizing Speaker Recognition Problems." Thesis, KTH, Optimeringslära och systemteori, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103821.
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Classi cation of data has many applications, amongst others within the eld of speaker recognition. Speaker recognition is the part of speech processing concerned with the task of automatically identifying or verifying speakers using dierent characteristics of their voices. The main focus in speaker recognition is to nd methods that separate data, in order to dierentiate between dierent speakers. In this thesis, such a method is obtained by building a support vector machine, which has proved to be a very good tool for separating all kinds of data. The rst version of the support vector machine is used to separate linearly separable data using linear hyperplanes, and it is then modi ed to separate linearly non-separable data, by allowing some data points to be misclassi ed. Finally, the support vector machine is improved further, through a generalization to higher dimensional data and by the use of dierent kernels and thus higher order hyperplanes. The developed support vector machine is in the end used on a set of speaker recognition data. The separation of two speakers are not very satisfying, most likely due to the very limited set of data. However, the results are very good when the support vector machine is used on other, more complete, sets of data.Klassi cering av data har manga anvandningsomraden, bland annat inom rostigenkanning. Rostigenkanning ar en del av talmodellering som behandlar problemet med att kunna identi era talare och veri era en talares identitet med hjalp av karakteristiska drag hos dennes rost. Fokus ligger pa att hitta metoder som kan separera data, for att sedan kunna separera talare. I detta kandidatexamensarbete byggs, for detta syfte, en support vector machine som has visats vara ett bra satt att separera olika data. Den forsta versionen anvands pa data som ar linjart separerbart i tva dimensioner, sedan utvecklas den till att kunna separera data som inte ar linjart separerbart, genom att tillata vissa datapunkter att bli felklassi cerade. Slutligen modi eras denna support vector machine till att kunna separera data i hogre dimensioner, samt anvanda olika karnor for att ge separerande hyperplan av hogre ordning. Den fardiga versionen av denna support vector machine anvands till sist pa data for ett rostigenkanningsproblem. Resultatet av att separera tva talare var inte tillfredsstallande, dock skulle mer data fran olika talare ge ett battre resultat. Nar daretmot en annan, mer komplett, mangd av data anvands for att bygga denna support vector machine blir resultatet valdigt bra.
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Alham, Nasullah Khalid. "Parallelizing support vector machines for scalable image annotation." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5452.
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Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. In this thesis distributed computing paradigms have been investigated to speed up SVM training, by partitioning a large training dataset into small data chunks and process each chunk in parallel utilizing the resources of a cluster of computers. A resource aware parallel SVM algorithm is introduced for large scale image annotation in parallel using a cluster of computers. A genetic algorithm based load balancing scheme is designed to optimize the performance of the algorithm in heterogeneous computing environments. SVM was initially designed for binary classifications. However, most classification problems arising in domains such as image annotation usually involve more than two classes. A resource aware parallel multiclass SVM algorithm for large scale image annotation in parallel using a cluster of computers is introduced. The combination of classifiers leads to substantial reduction of classification error in a wide range of applications. Among them SVM ensembles with bagging is shown to outperform a single SVM in terms of classification accuracy. However, SVM ensembles training are notably a computationally intensive process especially when the number replicated samples based on bootstrapping is large. A distributed SVM ensemble algorithm for image annotation is introduced which re-samples the training data based on bootstrapping and training SVM on each sample in parallel using a cluster of computers. The above algorithms are evaluated in both experimental and simulation environments showing that the distributed SVM algorithm, distributed multiclass SVM algorithm, and distributed SVM ensemble algorithm, reduces the training time significantly while maintaining a high level of accuracy in classifications.
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Yang, Jianqiang. "Classification under input uncertainty with support vector machines." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/69530/.
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Uncertainty can exist in any measurement of data describing the real world. Many machine learning approaches attempt to model any uncertainty in the form of additive noise on the target, which can be effective for simple models. However, for more complex models, and where a richer description of anisotropic uncertainty is available, these approaches can suffer. The principal focus of this thesis is the development of advanced classification approaches that can incorporate the known input uncertainties into support vector machines (SVMs), which can accommodate isotropic uncertain information in the classification. This new method is termed as uncertainty support vector classification (USVC). Kernel functions can be used as well through the derivation of a novel kernelisation formulation to generalise this proposed technique to non-linear models and the resulting optimisation problem is a second order cone program (SOCP) with a unique solution. Based on the statistical models on the input uncertainty, Bi and Zhang (2005) developed total support vector classification (TSVC), which has a similar geometric interpretation and optimisation formulation to USVC, but chooses much lower probabilities that the corresponding original inputs are going to be correctly classified by the optimal solution than USVC. Adaptive uncertainty support vector classification (AUSVC) is then developed based on the combination of TSVC and USVC, in which the probabilities of the original inputs being correctly classified are adaptively adjusted in accordance with the corresponding uncertain inputs. Inheriting the advantages from AUSVC and the minimax probability machine (MPM), minimax probability support vector classification (MPSVC) is developed to maximise the probabilities of the original inputs being correctly classified. Statistical tests are used to evaluate the experimental results of different approaches. Experiments illustrate that AUSVC and MPSVC are suitable for classifying the observed uncertain inputs and recovering the true target function respectively since the contamination is normally unknown for the learner.
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Andreola, Rafaela. "Support Vector Machines na classificação de imagens hiperespectrais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/17894.
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É de conhecimento geral que, em alguns casos, as classes são espectralmente muito similares e que não é possível separá-las usando dados convencionais em baixa dimensionalidade. Entretanto, estas classes podem ser separáveis com um alto grau de acurácia em espaço de alta dimensão. Por outro lado, classificação de dados em alta dimensionalidade pode se tornar um problema para classificadores paramétricos, como o Máxima Verossimilhança Gaussiana (MVG). Um grande número de variáveis que caracteriza as imagens hiperespectrais resulta em um grande número de parâmetros a serem estimados e, geralmente, tem-se um número limitado de amostras de treinamento disponíveis. Essa condição causa o fenômeno de Hughes que consiste na gradual degradação da acurácia com o aumento da dimensionalidade dos dados. Neste contexto, desperta o interesse a utilização de classificadores não-paramétricos, como é o caso de Support Vector Machines (SVM). Nesta dissertação é analisado o desempenho do classificador SVM quando aplicado a imagens hiperespectrais de sensoriamento remoto. Inicialmente os conceitos teóricos referentes à SVM são revisados e discutidos. Em seguida, uma série de experimentos usando dados AVIRIS são realizados usando diferentes configurações para o classificador. Os dados cobrem uma área de teste da Purdue University e apresenta classes de culturas agrícolas espectralmente muito similares. A acurácia produzida na classificação por diferentes kernels são investigadas em função da dimensionalidade dos dados e comparadas com as obtidas com o classificador MVG. Como SVM é aplicado a um par de classes por vez, desenvolveu-se um classificador multi-estágio estruturado em forma de árvore binária para lidar como problema multi-classe. Em cada nó, a seleção do par de classes mais separáveis é feita pelo critério distância de Bhattacharyya. Tais classes darão origem aos nós descendentes e serão responsáveis por definir a função de decisão SVM. Repete-se este procedimento em todos os nós da árvore, até que reste apenas uma classe por nó, nos chamados nós terminais. Os softwares necessários foram desenvolvidos em ambiente MATLAB e são apresentados na dissertação. Os resultados obtidos nos experimentos permitem concluir que SVM é uma abordagem alternativa válida e eficaz para classificação de imagens hiperespectrais de sensoriamento remoto.This dissertation deals with the application of Support Vector Machines (SVM) to the classification of remote sensing high-dimensional image data. It is well known that in many cases classes that are spectrally very similar and thus not separable when using the more conventional low-dimensional data, can nevertheless be separated with an high degree of accuracy in high dimensional spaces. Classification of high-dimensional image data can, however, become a challenging problem for parametric classifiers such as the well-known Gaussian Maximum Likelihood. A large number of variables produce an also large number of parameters to be estimated from a generally limited number of training samples. This condition causes the Hughes phenomenon which consists in a gradual degradation of the accuracy as the data dimensionality increases beyond a certain value. Non-parametric classifiers present the advantage of being less sensitive to this dimensionality problem. SVM has been receiving a great deal of attention from the international community as an efficient classifier. In this dissertation it is analyzed the performance of SVM when applied to remote sensing hyper-spectral image data. Initially the more theoretical concepts related to SVM are reviewed and discussed. Next, a series of experiments using AVIRIS image data are performed, using different configurations for the classifier. The data covers a test area established by Purdue University and presents a number of classes (agricultural fields) which are spectrally very similar to each other. The classification accuracy produced by different kernels is investigated as a function of the data dimensionality and compared with the one yielded by the well-known Gaussian Maximum Likelihood classifier. As SVM apply to a pair of classes at a time, a multi-stage classifier structured as a binary tree was developed to deal with the multi-class problem. The tree classifier is initially defined by selecting at each node the most separable pair of classes by using the Bhattacharyya distance as a criterion. These two classes will then be used to define the two descending nodes and the corresponding SVM decision function. This operation is performed at every node across the tree, until the terminal nodes are reached. The required software was developed in MATLAB environment and is also presented in this dissertation.
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Bordes, Antoine. "New algorithms for large-scale support vector machines." Paris 6, 2010. http://www.theses.fr/2010PA066011.
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Il existe un besoin certain dans la communauté de l'apprentissage statistique pour des méthodes capables d'être entraînées sur des ensembles d'apprentissage de grande échelle, et pouvant ainsi gérer les quantités colossales d'informations générées quotidiennement. Dans cette thèse, nous proposons des solutions pour réduire le temps d'entraînement et les besoins en mémoire d'algorithmes d'apprentissage sans pour autant dégrader leur précision. Nous nous intéressons en particulier aux Machines à Vecteurs Supports (SVMs), des méthodes populaires utilisées en général pour des tâches de classification automatique mais qui peuvent être adaptées à d'autres applications. Nous étudions tout d'abord le processus d'apprentissage par descente de gradient stochastique pour les SVMs linéaires. Cela nous amène à définir et étudier le nouvel algorithme, SGD-QN. Après cela, nous introduisons une nouvelle procédure d'apprentissage: le principe du "Process/Reprocess" que nous déclinons dans trois algorithmes. Le Huller et LASVM servent à apprendre des SVMs destinés à traiter des problèmes de classification binaire. Pour la tâche plus complexe de prédiction de sorties structurées, nous modifions en profondeur l'algorithme LaSVM, ce qui conduit à l'algorithme LaRank. Notre dernière contribution concerne le problème récent de l'apprentissage avec une supervision ambiguë pour lequel nous proposons un nouveau cadre théorique (et un algorithme associé). Nous l'appliquons au problème de l'étiquetage sémantique du langage. Tous les algorithmes introduits dans cette thèse atteignent les performances de l'état-de l'art, en particulier en ce qui concerne les vitesses d'entraînement.
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Melki, Gabriella A. "Fast Online Training of L1 Support Vector Machines." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4282.
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This thesis proposes a novel experimental environment (non-linear stochastic gradient descent, NL-SGD), as well as a novel online learning algorithm (OL SVM), for solving a classic nonlinear Soft Margin L1 Support Vector Machine (SVM) problem using a Stochastic Gradient Descent (SGD) algorithm. The NL-SGD implementation has a unique method of random sampling and alpha calculations. The developed code produces a competitive accuracy and speed in comparison with the solutions of the Direct L2 SVM obtained by software for Minimal Norm SVM (MN-SVM) and Non-Negative Iterative Single Data Algorithm (NN-ISDA). The latter two algorithms have shown excellent performances on large datasets; which is why we chose to compare NL-SGD and OL SVM to them. All experiments have been done under strict double (nested) cross-validation, and the results are reported in terms of accuracy and CPU times. OL SVM has been implemented within MATLAB and is compared to the classic Sequential Minimal Optimization (SMO) algorithm implemented within MATLAB's solver, fitcsvm. The experiments with OL SVM have been done using k-fold cross-validation and the results reported in % error and % speedup of CPU Time.
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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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Yuan, Yu. "Image-based gesture recognition with support vector machines." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 155 p, 2008. http://proquest.umi.com/pqdweb?did=1601517921&sid=2&Fmt=2&clientId=8331&RQT=309&VName=PQD.
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Shakeel, Mohammad Danish. "Land Cover Classification Using Linear Support Vector Machines." Connect to resource online, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1231812653.
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Melki, Gabriella A. "Novel Support Vector Machines for Diverse Learning Paradigms." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5630.
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This dissertation introduces novel support vector machines (SVM) for the following traditional and non-traditional learning paradigms: Online classification, Multi-Target Regression, Multiple-Instance classification, and Data Stream classification.
Three multi-target support vector regression (SVR) models are first presented. The first involves building independent, single-target SVR models for each target. The second builds an ensemble of randomly chained models using the first single-target method as a base model. The third calculates the targets' correlations and forms a maximum correlation chain, which is used to build a single chained SVR model, improving the model's prediction performance, while reducing computational complexity.
Under the multi-instance paradigm, a novel SVM multiple-instance formulation and an algorithm with a bag-representative selector, named Multi-Instance Representative SVM (MIRSVM), are presented. The contribution trains the SVM based on bag-level information and is able to identify instances that highly impact classification, i.e. bag-representatives, for both positive and negative bags, while finding the optimal class separation hyperplane. Unlike other multi-instance SVM methods, this approach eliminates possible class imbalance issues by allowing both positive and negative bags to have at most one representative, which constitute as the most contributing instances to the model.
Due to the shortcomings of current popular SVM solvers, especially in the context of large-scale learning, the third contribution presents a novel stochastic, i.e. online, learning algorithm for solving the L1-SVM problem in the primal domain, dubbed OnLine Learning Algorithm using Worst-Violators (OLLAWV). This algorithm, unlike other stochastic methods, provides a novel stopping criteria and eliminates the need for using a regularization term. It instead uses early stopping. Because of these characteristics, OLLAWV was proven to efficiently produce sparse models, while maintaining a competitive accuracy.
OLLAWV's online nature and success for traditional classification inspired its implementation, as well as its predecessor named OnLine Learning Algorithm - List 2 (OLLA-L2), under the batch data stream classification setting. Unlike other existing methods, these two algorithms were chosen because their properties are a natural remedy for the time and memory constraints that arise from the data stream problem. OLLA-L2's low spacial complexity deals with memory constraints imposed by the data stream setting, and OLLAWV's fast run time, early self-stopping capability, as well as the ability to produce sparse models, agrees with both memory and time constraints. The preliminary results for OLLAWV showed a superior performance to its predecessor and was chosen to be used in the final set of experiments against current popular data stream methods.
Rigorous experimental studies and statistical analyses over various metrics and datasets were conducted in order to comprehensively compare the proposed solutions against modern, widely-used methods from all paradigms. The experimental studies and analyses confirm that the proposals achieve better performances and more scalable solutions than the methods compared, making them competitive in their respected fields.
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Lin, Chun-fu, and 林俊甫. "Fuzzy Support Vector Machines." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/03738600554973780641.
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博士國立臺灣大學
電機工程學研究所
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A support vector machine learns the decision surface from the input points for classification problems or regression problems. In many applications, each input point may not be fully assigned to the same importance in the training process. In this thesis, we apply a fuzzy membership to each input point and reformulate the support vector machines such that different input points can make different contributions to the learning of the decision surface. We call the proposed method fuzzy support vector machines or FSVMs. FSVMs provides a method for the classification problem with noises or outliers. However, there is no general rule to determine the membership of each data point. We can manually associate each data point with a fuzzy membership that can reflect their relative degrees as meaningful data. To enable automatic setting of memberships, we introduce two factors in training data points, the confident factor and the trashy factor, and automatically generate fuzzy memberships of training data points from a heuristic strategy by using these two factors and a mapping function. We investigate and compare two strategies in the experiments and the results show that the generalization error of FSVMs is comparable to other methods on benchmark datasets. The proposed approach for automatic setting of fuzzy memberships makes the FSVMs more applicable in reducing the effects of noises or outliers.
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"Support Vector Machines in R." Department of Statistics and Mathematics, 2005. http://epub.wu-wien.ac.at/dyn/dl/wp/epub-wu-01_89f.
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