Thematische Bibliographien / Multi-class classifiers / Dissertationen
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Dissertationen zum Thema „Multi-class classifiers“

Autor: Grafiati
Veröffentlicht am 28. Juni 2021
Zuletzt aktualisiert am 29. Juli 2025

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1

Kybartas, Rimantas. "Multi-class recognition using pair-wise classifiers." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20101001_150424-92661.

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There are plenty of solutions for the task of multi-class recognition. Unfortunately, these solutions are not always unanimous. Most of them are based on empirical experiments while statistical data features consideration is often omitted. That’s why questions like when and which method should be used, what the reliability of any chosen method is for solving a multi-class recognition task arise. In this dissertation two-stage multi-class decision methods are analyzed. Pair-wise classifiers able to better exploit statistical data features are used in the first stage of such methods. In the seco
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2

Abd, Rahman Mohd Amiruddin. "Kernel and multi-class classifiers for multi-floor WLAN localisation." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/13768/.

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Indoor localisation techniques in multi-floor environments are emerging for location based service applications. Developing an accurate location determination and time-efficient technique is crucial for online location estimation of the multi-floor localisation system. The localisation accuracy and computational complexity of the localisation system mainly relies on the performance of the algorithms embedded with the system. Unfortunately, existing algorithms are either time-consuming or inaccurate for simultaneous determination of floor and horizontal locations in multi-floor environment. Thi
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Beneš, Jiří. "Unární klasifikátor obrazových dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442432.

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The work deals with an introduction to classification algorithms. It then divides classifiers into unary, binary and multi-class and describes the different types of classifiers. The work compares individual classifiers and their areas of use. For unary classifiers, practical examples and a list of used architectures are given in the work. The work contains a chapter focused on the comparison of the effects of hyper parameters on the quality of unary classification for individual architectures. Part of the submission is a practical example of reimplementation of the unary classifier.
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Odabai, Fard Seyed Hamidreza. "Efficient multi-class objet detection with a hierarchy of classes." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22623/document.

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Dans cet article, nous présentons une nouvelle approche de détection multi-classes basée sur un parcours hiérarchique de classifieurs appris simultanément. Pour plus de robustesse et de rapidité, nous proposons d’utiliser un arbre de classes d’objets. Notre modèle de détection est appris en combinant les contraintes de tri et de classification dans un seul problème d’optimisation. Notre formulation convexe permet d’utiliser un algorithme de recherche pour accélérer le temps d’exécution. Nous avons mené des évaluations de notre algorithme sur les benchmarks PASCAL VOC (2007 et 2010). Comparé à
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Verschae, Tannenbaum Rodrigo. "Object Detection Using Nested Cascades of Boosted Classifiers. A Learning Framework and Its Extension to The Multi-Class Case." Tesis, Universidad de Chile, 2010. http://www.repositorio.uchile.cl/handle/2250/102398.

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6

Mauricio-Sanchez, David, Andrade Lopes Alneu de, and higuihara Juarez Pedro Nelson. "Approaches based on tree-structures classifiers to protein fold prediction." Institute of Electrical and Electronics Engineers Inc, 2017. http://hdl.handle.net/10757/622536.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different superv
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Abdelhamid, Neda. "Deriving classifiers with single and multi-label rules using new Associative Classification methods." Thesis, De Montfort University, 2013. http://hdl.handle.net/2086/10120.

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Associative Classification (AC) in data mining is a rule based approach that uses association rule techniques to construct accurate classification systems (classifiers). The majority of existing AC algorithms extract one class per rule and ignore other class labels even when they have large data representation. Thus, extending current AC algorithms to find and extract multi-label rules is promising research direction since new hidden knowledge is revealed for decision makers. Furthermore, the exponential growth of rules in AC has been investigated in this thesis aiming to minimise the number o
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Son, Kyung-Im. "A multi-class, multi-dimensional classifier as a topology selector for analog circuit design / by Kyung-Im Son." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/5919.

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9

Bautista, Martín Miguel Ángel. "Learning error-correcting representations for multi-class problems." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/396124.

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Real life is full of multi-class decision tasks. In the Pattern Recognition field, several method- ologies have been proposed to deal with binary problems obtaining satisfying results in terms of performance. However, the extension of very powerful binary classifiers to the multi-class case is a complex task. The Error-Correcting Output Codes framework has demonstrated to be a very powerful tool to combine binary classifiers to tackle multi-class problems. However, most of the combinations of binary classifiers in the ECOC framework overlook the underlay- ing structure of the multi-class problem.
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Rocha, Anderson de Rezende 1980. "Classificadores e aprendizado em processamento de imagens e visão computacional." [s.n.], 2009. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276019.

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Orientador: Siome Klein Goldenstein<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto da Computação<br>Made available in DSpace on 2018-08-12T17:37:15Z (GMT). No. of bitstreams: 1 Rocha_AndersondeRezende_D.pdf: 10303487 bytes, checksum: 243dccfe5255c828ce7ead27c27eb1cd (MD5) Previous issue date: 2009<br>Resumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional.
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Giovannone, Carrie Lynn. "A Longitudinal Study of School Practices and Students’ Characteristics that Influence Students' Mathematics and Reading Performance of Arizona Charter Middle Schools." Kent State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=kent1288808181.

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12

Huang, Tian-Liang, and 黃天亮. "Comparison of L2-Regularized Multi-Class Linear Classifiers." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/25699807732878797831.

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碩士<br>臺灣大學<br>資訊工程學研究所<br>98<br>The classification problem appears in many applications such as document classification and web page search. Support vector machine(SVM) is one of the most popular tools used in classification task. One of the component in SVM is the kernel trick. We use kernels to map data into a higher dimentional space. And this technique is applied in non-linear SVMs. For large-scale sparce data, we use the linear kernel to deal with it. We call such SVM as the linear SVM. There are many kinds of SVMs in which different loss functions are applied. We call these SVMs as L1-SV
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Bourke, Christopher M. "Contributions to computational complexity and machine learning unambiguity in log-space computations and reoptimizing multi-class classifiers /." 2008. http://proquest.umi.com/pqdweb?did=1650513281&sid=22&Fmt=2&clientId=14215&RQT=309&VName=PQD.

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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2008.<br>Title from title screen (site viewed Mar. 10, 2009). PDF text: vi, 77 p. : ill. ; 634 K. UMI publication number: AAT 3336828. Includes bibliographical references. Also available in microfilm and microfiche formats.
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Tung, Chun-Hsien, and 董純賢. "Adopting the framework of Multi-level Class Priority with Multiple Classifiers to improve the Accuracy of Text Classification." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/43710491878925013881.

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碩士<br>淡江大學<br>資訊工程學系碩士在職專班<br>98<br>Regardless that the associative classification (AC) [1][2] method normally ranks the sequence according to the prescribed criteria, yet in terms of the problem of rule dependency that exists between rules, under the identical confidence value, support value and length criteria, the sequence by which the rules are executed can still impact the classification results. The core of the thesis, focusing on rule ranking problems, entails for more than adopting the Lazy[3] method as the general ranking principle for conducting document classification focusing o
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Tang, Hau-Ju, and 湯皓如. "Multi-class Iterative Minimum-Squared-Error Discriminant Classifier." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/06752037310233686648.

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碩士<br>國立臺灣大學<br>工業工程學研究所<br>95<br>Discriminant classifier is a type of supervised machine learning technique. There are two approaches to it. One is the Fisher’s discriminant; the other is the Minimum-Squared-Error (MSE) discriminant. The MSE discriminant is usually used to deal with two-class problems. The multi-class MSE approach extends the MSE discriminant to allow problems with more than two classes by providing a set of orthonormal class-label vectors through the Gram-Schmidt process. The resulting class-label vectors are made orthonormal so that the discriminants can be orthogonal as we
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Tang, Hau-Ju. "Multi-class Iterative Minimum-Squared-Error Discriminant Classifier." 2007. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2207200723525900.

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17

Chen-Wei, Li. "Effective Multi-class Kernel MSE Classifier with Sherman-Woodbury Formula." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200616565800.

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18

Li, Chen-Wei, and 李振維. "Effective Multi-class Kernel MSE Classifier with Sherman-Woodbury Formula." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/10779969000677760449.

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碩士<br>國立臺灣大學<br>工業工程學研究所<br>94<br>In general, there are two kinds of linear classification methods: one is MSE, and the other is FLD. Because linear methods are not sufficient to analyze the data with nonlinear patterns, the nonlinear methods KMSE and KFD are hence developed from MSE and FLD, respectively. Both transform the instances from the original attribute space to the high-dimensional feature space and then linear methods are applied. The objective of FLD and KFD is to find the directions on which the projection of training instances can provide the maximal separability of classes. FLD
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Lai, Yu-Han, and 賴妤函. "Using of High-Efficient Multivariate Classifier for Multi-class Classification Problems." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/a5me32.

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碩士<br>臺中技術學院<br>流通管理系碩士班<br>99<br>This paper proposes a novel classification model in dealing with multi-class problems when confronting large scales of features and instances. Our classification model comprises four stages: feature evaluation, feature selection, feature extraction and inductive learning. In the first stage, the investigation of data diversity enhances the classification effect of Information Gain. The second stage strengthens the relevance analysis by introducing correlation analysis and then serves a more reliable mechanism for feature selection. In the third stage, principa
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20

Cheng, Wei-Lun, and 鄭為倫. "The Research on A Single Classifier in Text Classification of Multi-Class." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/374xm4.

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碩士<br>銘傳大學<br>資訊管理學系碩士班<br>93<br>On the research of performance of automatic text classification, the number of term selection that influence the performance of text classification. There are many researches which done terms extraction in the past. But in the period of our research, we detected that in the text of terms with low weight which can’t increase the performance of text classification, on the contrary become noise to reduce the accuracy. In addition, on the research of text classification, there are many kinds of classifiers has been developed. The performance of different classifier
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21

(6259343), Xiaodong Hou. "Distributed Solutions for a Class of Multi-agent Optimization Problems." Thesis, 2019.

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Distributed optimization over multi-agent networks has become an increasingly popular research topic as it incorporates many applications from various areas such as consensus optimization, distributed control, network resource allocation, large scale machine learning, etc. Parallel distributed solution algorithms are highly desirable as they are more scalable, more robust against agent failure, align more naturally with either underlying agent network topology or big-data parallel computing framework. In this dissertation, we consider a multi-agent optimization formulation where the global obj
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Wei, Li, and 劉力瑋. "Using Over-sampling and Multi-classifier Committee Approach for skewed class distribution – a case study of diagnosis model construction of Benign prostate hypertrophy and Cancer of prostate." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/78752271295978172163.

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碩士<br>國立中正大學<br>資訊管理所<br>95<br>Regarding the non-skewed distribution, to utilize the existing data mining classification to construct the prediction model can reach a certain level of prediction accuracy. However, in the real data mining case, the dataset distribution is always skewed distribution. In clinical case, because the number of healthy people is more than the number of unhealthy people, the collected data would be congenital skewed distribution. If we utilize those dataset with skewed distribution to construct the prediction model, the prediction deviation should be a big problem. Th
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(9739226), Akhil Prasad. "MULTI-OBJECTIVE DESIGN OF DYNAMIC WIRELESS CHARGING SYSTEMS FOR HEAVY – DUTY VEHICLES." Thesis, 2020.

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<p>Presently, internal combustion engines provide power to move the majority of vehicles on the roadway. While battery-powered electric vehicles provide an alternative, their widespread acceptance is hindered by range anxiety and longer charging/refueling times. Dynamic wireless power transfer (DWPT) has been proposed as a means to reduce both range anxiety and charging/refueling times. In DWPT, power is provided to a vehicle in motion using electromagnetic fields transmitted by a transmitter embedded within the roadway to a receiver at the underside of the vehicle. For commercial vehicles, D
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