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Thematische Bibliographien / Pattern recognition / Dissertationen

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Dissertationen zum Thema „Pattern recognition“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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1

An, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.

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The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.

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2

Yao, Xiaoqiang. "Pattern-recognition scheduling." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1177698616.

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3

Petheram, R. J. "Automatic pattern recognition." Thesis, University of Nottingham, 1989. http://eprints.nottingham.ac.uk/28974/.

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In this thesis the author presents a new method for the location, extraction and normalisation of discrete objects found in digital images. The extraction is by means of sub-pixcel contour following around the object. The normalisation obtains and removes the information concerning size, orientation and location of the object within an image. Analyses of the results are carried out to determine the confidence in recognition of patterns, and methods of cross correlation of object descriptions using Fourier transforms are demonstrated.

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4

Choakjarernwanit, Naruetep. "Statistical pattern recognition." Thesis, University of Surrey, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306586.

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5

Placide, Eustache. "Hybrid pattern recognition." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 1987. http://digitalcommons.auctr.edu/dissertations/3018.

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There are two basic approaches to pattern recognition: decision-theoretic and syntactic. However, in actual applications, a combination of both may be needed. One such hybrid technique consists of syntactic method coupled with stochasticity in its grammar. Randomness in the syntactic case is caused due to noise and insufficient information about characteristics of pattern classes. To absorb the effect of this randomness, the grammar must be generalized to include the probabilities of production rules. In this paper, a preliminary discussion of issues involved with hybrid techniques, in general

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6

Smeraldi, Fabrizio. "Attention-driven pattern recognition /." [S.l.] : [s.n.], 2000. http://library.epfl.ch/theses/?nr=2153.

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7

Angstenberger, Larisa. "Dynamic fuzzy pattern recognition." [S.l.] : [s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962701106.

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8

Weir, D. K. "Pattern recognition of electrocardiograms." Thesis, University of Ulster, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355922.

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9

Lindén, Fredrik. "Fractal pattern recognition and recreation." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-181224.

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It speaks by itself that in order to find oil, one must know where to look for it. In this thesis I have investigated and created new tools to find salt in the bedrock, and to recreate images according to some parameters, (fractal dimension and lacunarity). The oil prospecting company Schlumberger gathers nowadays a huge amount of seismic information. It is very time consuming to interpret the seismic data by hand. My task is to find a good way to detect salt in the seismic images of the underworld, that can then be used to classify the seismic data. The theory indicates that the salt behaves

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10

Sun, Te-Wei. "DEPARS, design pattern recognition system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq28464.pdf.

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11

Hoang, Thai V. "Image Representations for Pattern Recognition." Phd thesis, Université Nancy II, 2011. http://tel.archives-ouvertes.fr/tel-00714651.

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La pertinence d'une application de traitement de signal relève notamment du choix d'une "représentation adéquate''. Par exemple, pour la reconnaissance de formes, la représentation doit mettre en évidence les propriétés salientes d'un signal; en débruitage, permettre de séparer le signal du bruit; ou encore en compression, de synthétiser fidèlement le signal d'entrée à l'aide d'un nombre réduit de coefficients. Bien que les finalités de ces quelques traitements soient distinctes, il apparait clairement que le choix de la représentation impacte sur les performances obtenues. La représentation d

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12

Wong, K. H. "Dynamic programming in pattern recognition." Thesis, University of Cambridge, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383059.

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13

Kinna, David John. "Pattern recognition in chemical crystallography." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318724.

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14

Daemi, M. F. "Information theory and pattern recognition." Thesis, University of Nottingham, 1990. http://eprints.nottingham.ac.uk/14003/.

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This thesis presents an account of an investigation into the use of information theory measures in pattern recognition problems. The objectives were firstly to determine the information content of the set of representations of an input image which are found at the output of an array of sensors; secondly to assess the information which may be used to allocate different patterns to appropriate classes in order to provide a means of recognition; and thirdly to assess the recognition capability of pattern recognition systems and their efficiency of utilization of information. Information assessmen

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15

Sardana, H. K. "Edge moments in pattern recognition." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.357101.

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16

Füllen, Georg Karl-Heinz. "Protein engineering and pattern recognition." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/17354.

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17

STOSIC, Dusan. "q-Gaussians for pattern recognition." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/17361.

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Submitted by Isaac Francisco de Souza Dias (isaac.souzadias@ufpe.br) on 2016-07-13T19:23:52Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dusan Stosic - dissertacao de mestrado.pdf: 6434406 bytes, checksum: db312999879f1c3ebb1795ce764a272e (MD5)<br>Made available in DSpace on 2016-07-13T19:23:52Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dusan Stosic - dissertacao de mestrado.pdf: 6434406 bytes, checksum: db312999879f1c3ebb1795ce764a272e (MD5) Previous issue date: 2016-03-01<br

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18

Ihnatenko, N. V. "Systems for automatic pattern recognition." Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/34837.

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Pattern recognition aims to make the process of learning and detection of patterns explicit, such that it can partially or entirely be implemented on computers. Automatic (machine) recognition, description, classification (grouping of patterns into pattern classes) have become important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. In almost any area of science in which observations are studied but the underlying mathematical or statistical models are not available,

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19

Dover, Kathryn. "Pattern Recognition in Stock Data." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/hmc_theses/105.

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Finding patterns in high dimensional data can be difficult because it cannot be easily visualized. There are many different machine learning methods to fit data in order to predict and classify future data but there is typically a large expense on having the machine learn the fit for a certain part of a dataset. We propose a geometric way of defining different patterns in data that is invariant under size and rotation. Using a Gaussian Process, we find that pattern within stock datasets and make predictions from it.

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20

Martino, Federico De. "Pattern recognition of brain signals." [Maastricht] : [Maastricht University], 2008. http://arno.unimaas.nl/show.cgi?fid=13359.

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21

Burles, Nathan. "Pattern recognition using associative memories." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/7368/.

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The human brain is extremely effective at performing pattern recognition, even in the presence of noisy or distorted inputs. Artificial neural networks attempt to imitate the structure of the brain, often with a view to mimicking its success. The binary correlation matrix memory (CMM) is a particular type of neural network that is capable of learning and recalling associations extremely quickly, as well as displaying a high storage capacity and having the ability to generalise from patterns already learned. CMMs have been used as a major component of larger architectures designed to solve a wi

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22

Calvo-Zaragoza, Jorge. "Pattern Recognition for Music Notation." Doctoral thesis, Universidad de Alicante, 2016. http://hdl.handle.net/10045/63415.

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23

UL-ISLAM, IHTESHAM. "Feature Fusion for Pattern Recognition." Doctoral thesis, Politecnico di Torino, 2015. http://hdl.handle.net/11583/2592755.

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Information or data fusion is one of the solutions adopted for improving the performance of a pattern recognition system. Information can be gathered either from multiple data sources or through the use of multiple representations generated from a single data source. A single representation summarizes the information and provides a single cue on the data, and thus may not be able to fully reveal the inherent characteristics of the data. In visual recognition, image representations are generally categorized into global and local based types. A global representation captures features corresponds

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24

Ou, Chung-Pei. "Protein array for small molecules recognition using pattern recognition." Thesis, Imperial College London, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420941.

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25

Al, Rifaee Mustafa Moh'd Husien. "Unconstrained iris recognition." Thesis, De Montfort University, 2014. http://hdl.handle.net/2086/10949.

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This research focuses on iris recognition, the most accurate form of biometric identification. The robustness of iris recognition comes from the unique characteristics of the human, and the permanency of the iris texture as it is stable over human life, and the environmental effects cannot easily alter its shape. In most iris recognition systems, ideal image acquisition conditions are assumed. These conditions include a near infrared (NIR) light source to reveal the clear iris texture as well as look and stare constraints and close distance from the capturing device. However, the recognition a

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26

Robinson, Daniel D. "Applications of pattern recognition and pattern analysis to molecule design." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343465.

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27

Mankoff, Jennifer C. "An architecture and interaction techniques for handling ambiguity in recognition-based input." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/8214.

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28

Mahmood, A. "Automatic drawing recognition." Thesis, University of Nottingham, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.381072.

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29

Scott, Emily A. "Recognition of aerospace acoustic sources using advanced pattern recognition techniques." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-03022010-020131/.

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30

Suh, Bongwon. "Image management using pattern recognition systems." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2455.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.<br>Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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31

Hall, Arthur Daniel. "Pipelined image processing for pattern recognition." Thesis, University of Cambridge, 1992. https://www.repository.cam.ac.uk/handle/1810/251523.

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32

Colven, David Michael. "Tactile pattern recognition using neural networks." Thesis, University of Ottawa (Canada), 1993. http://hdl.handle.net/10393/6513.

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This thesis presents a system for the capture and recognition of tactile images using neural networks. Neural Networks utilizing the backpropagation technique are used to provide a general purpose recognition engine for several classes of pattern recognition problems. Examples of successful networks are presented with discussion of the results and methodology for development of each. The system consists of the following: Image capture, training of network using an iterative approach and testing of the network against independent images not present during training. Pattern capture is performed

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33

Zieba, Maciej. "Multistage neural networks for pattern recognition." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2087.

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In this work the concept of multistage neural networks is going to be presented. The possibility of using this type of structure for pattern recognition would be discussed and examined with chosen problem from eld area. The results of experiment would be confront with other possible methods used for the problem.

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34

Kleyko, Denis. "Pattern Recognition with Vector Symbolic Architectures." Licentiate thesis, Luleå tekniska universitet, Datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-17439.

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Pattern recognition is an area constantly enlarging its theoretical and practical horizons. Applications of pattern recognition and machine learning can be found in many areas of the present day world including health-care, robotics, manufacturing, economics, automation, transportation, etc. Despite some success in many domains pattern recognition algorithms are still far from being close to their biological vis-a-vis – human brain. New possibilities in the area of pattern recognition may be achieved by application of biologically inspired approaches. This thesis presents the usage of a bio-in

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35

Wang, Qun. "Bootstrap techniques for statistical pattern recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0027/MQ52407.pdf.

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36

Sherrah, Jamie. "Automatic feature extraction for pattern recognition /." Title page, contents and abstract only, 1998. http://web4.library.adelaide.edu.au/theses/09PH/09phs553.pdf.

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Thesis (Ph. D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1999.<br>CD-ROM in back pocket comprises experimental results and executables. Includes bibliographical references (p. 251-261).

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37

Surkov, David. "Inductive confidence machine for pattern recognition." Thesis, Royal Holloway, University of London, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412337.

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38

Elliffe, Martin C. M. "Neural networks for Invariant pattern recognition." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.302530.

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39

Ragothaman, Pradeep. "EFFICIENT ALGORITHMS FOR CORRELATION PATTERN RECOGNITION." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2132.

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The mathematical operation of correlation is a very simple concept, yet has a very rich history of application in a variety of engineering fields. It is essentially nothing but a technique to measure if and to what degree two signals match each other. Since this is a very basic and universal task in a wide variety of fields such as signal processing, communications, computer vision etc., it has been an important tool. The field of pattern recognition often deals with the task of analyzing signals or useful information from signals and classifying them into classes. Very often, these classes ar

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40

Thompson, J. R. "Applications of pattern recognition in medicine." Thesis, Open University, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.377939.

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41

Choakjarernwanit, Naruetep. "Feature selection in statistical pattern recognition." Thesis, University of Surrey, 1992. http://epubs.surrey.ac.uk/843569/.

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This thesis addresses the problem of feature selection in pattern recognition. A detailed analysis and an experimental comparison of various search strategies for selecting a feature set of size d from D available measurements are presented. For a realistic problem, optimal search, even if performed using the branch and bound search method, is computationally prohibitive. The alternative is to use suboptimal search methods. Of these, there are four methods, namely the sequential forward selection (SFS), sequential backward selection (SBS), sequential forward floating selection (SFFS), and sequ

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42

Zhendong, Wang. "Error Pattern Recognition Using Machine Learning." Thesis, Linköpings universitet, Statistik och maskininlärning, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150589.

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Mobile networks use automated continuous integration to secure the new technologies, which must reach high quality and backwards compatibility. The machinery needs to be constantly improved to meet the high demands that exist today and will evolve in the future. When testing products in large scale in a telecommunication environment, many parameters may be causing the error. Machine learning can help to assign troubleshooting labels and identify problematic areas in the test environment. In this thesis project, different modeling approaches will be applied step-wise. First, both the TF-IDF (te

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43

Lucas, Simon Mark. "Connectionist architectures for syntactic pattern recognition." Thesis, University of Southampton, 1991. https://eprints.soton.ac.uk/256263/.

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44

Kou, Yufeng. "Abnormal Pattern Recognition in Spatial Data." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.

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In the recent years, abnormal spatial pattern recognition has received a great deal of attention from both industry and academia, and has become an important branch of data mining. Abnormal spatial patterns, or spatial outliers, are those observations whose characteristics are markedly different from their spatial neighbors. The identification of spatial outliers can be used to reveal hidden but valuable knowledge in many applications. For example, it can help locate extreme meteorological events such as tornadoes and hurricanes, identify aberrant genes or tumor cells, discover highway traffic

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45

Tembe, Waibhav D. "Proximity Metrics for Contextual Pattern Recognition." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1096665126.

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46

Villegas, Santamaría Mauricio. "Contributions to High-Dimensional Pattern Recognition." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/10939.

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This thesis gathers some contributions to statistical pattern recognition particularly targeted at problems in which the feature vectors are high-dimensional. Three pattern recognition scenarios are addressed, namely pattern classification, regression analysis and score fusion. For each of these, an algorithm for learning a statistical model is presented. In order to address the difficulty that is encountered when the feature vectors are high-dimensional, adequate models and objective functions are defined. The strategy of learning simultaneously a dimensionality reduction function and the pat

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47

Siddique, Nahian A. "PATTERN RECOGNITION IN CLASS IMBALANCED DATASETS." VCU Scholars Compass, 2016. http://scholarscompass.vcu.edu/etd/4480.

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Class imbalanced datasets constitute a significant portion of the machine learning problems of interest, where recognizing the ‘rare class’ is the primary objective for most applications. Traditional linear machine learning algorithms are often not effective in recognizing the rare class. In this research work, a specifically optimized feed-forward artificial neural network (ANN) is proposed and developed to train from moderate to highly imbalanced datasets. The proposed methodology deals with the difficulty in classification task in multiple stages—by optimizing the training dataset, modifyi

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48

Suliman, Ayman, and Joakim Bäverlind. "Experiments With Four Pattern Recognition Algorithms." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214730.

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A big part of computer vision concerns the issue ofhow well images can be classified into their corresponding classes.Image classification is a big part of reducing the gap betweenhuman and AI performance. Images are classified by first usinga dataset of images together with their given classifications totrain the system with machine learning. This can then be usedon images without any classification to test and see how well thealgorithm can classify an unknown image. However, this can bedone in many different ways and methods. The aim of the projectis to compare the performance of four algori

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49

El, Ghawalby Heyayda. "Spectral geometry for structural pattern recognition." Thesis, University of York, 2011. http://etheses.whiterose.ac.uk/1525/.

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Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects. However, the analysis of data in this form has proved an elusive problem; for instance, it suffers from the robustness to structural noise. One way to circumvent this problem is to embed the nodes of a graph in a vector space and to study the properties of the point distribution that results from the embedding. This is a problem that arises in a number of areas incl

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50

Dannenberg, Matthew. "Pattern Recognition in High-Dimensional Data." Scholarship @ Claremont, 2016. https://scholarship.claremont.edu/hmc_theses/76.

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Vast amounts of data are produced all the time. Yet this data does not easily equate to useful information: extracting information from large amounts of high dimensional data is nontrivial. People are simply drowning in data. A recent and growing source of high-dimensional data is hyperspectral imaging. Hyperspectral images allow for massive amounts of spectral information to be contained in a single image. In this thesis, a robust supervised machine learning algorithm is developed to efficiently perform binary object classification on hyperspectral image data by making use of the geometry of

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