Relevant bibliographies by topics / ANN Classifiers

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  1. Dissertations / Theses

Academic literature on the topic 'ANN Classifiers'

Author: Grafiati
Published: 6 September 2023

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Dissertations / Theses on the topic "ANN Classifiers"

1

Eldud, Omer Ahmed Abdelkarim. "Prediction of protein secondary structure using binary classificationtrees, naive Bayes classifiers and the Logistic Regression Classifier." Thesis, Rhodes University, 2016. http://hdl.handle.net/10962/d1019985.

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The secondary structure of proteins is predicted using various binary classifiers. The data are adopted from the RS126 database. The original data consists of protein primary and secondary structure sequences. The original data is encoded using alphabetic letters. These data are encoded into unary vectors comprising ones and zeros only. Different binary classifiers, namely the naive Bayes, logistic regression and classification trees using hold-out and 5-fold cross validation are trained using the encoded data. For each of the classifiers three classification tasks are considered, namely helix
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2

Joo, Hyonam. "Binary tree classifier and context classifier." Thesis, Virginia Polytechnic Institute and State University, 1985. http://hdl.handle.net/10919/53076.

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Two methods of designing a point classifier are discussed in this paper, one is a binary decision tree classifier based on the Fisher's linear discriminant function as a decision rule at each nonterminal node, and the other is a contextual classifier which gives each pixel the highest probability label given some substantially sized context including the pixel. Experiments were performed both on a simulated image and real images to illustrate the improvement of the classification accuracy over the conventional single-stage Bayes classifier under Gaussian distribution assumption.<br>Master of
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3

Billing, Jeffrey J. (Jeffrey Joel) 1979. "Learning classifiers from medical data." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/8068.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (leaf 32).<br>The goal of this thesis was to use machine-learning techniques to discover classifiers from a database of medical data. Through the use of two software programs, C5.0 and SVMLight, we analyzed a database of 150 patients who had been operated on by Dr. David Rattner of the Massachusetts General Hospital. C5.0 is an algorithm that learns decision trees from data while SVMLight learns support vector machines from the data
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4

Siegel, Kathryn I. (Kathryn Iris). "Incremental random forest classifiers in spark." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106105.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (page 53).<br>The random forest is a machine learning algorithm that has gained popularity due to its resistance to noise, good performance, and training efficiency. Random forests are typically constructed using a static dataset; to accommodate new data, random forests are usually regrown. This thesis presents two main strategies for updating random forests incrementally, rather than entirely re
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5

Palmer-Brown, Dominic. "An adaptive resonance classifier." Thesis, University of Nottingham, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334802.

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Xue, Jinghao. "Aspects of generative and discriminative classifiers." Thesis, Connect to e-thesis, 2008. http://theses.gla.ac.uk/272/.

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Thesis (Ph.D.) - University of Glasgow, 2008.<br>Ph.D. thesis submitted to the Department of Statistics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references. Print version also available.
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7

Frankowsky, Maximilian, and Dan Ke. "Humanness and classifiers in Mandarin Chinese." Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-224789.

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Mandarin Chinese numeral classifiers receive considerable at-tention in linguistic research. The status of the general classifier 个 gè re-mains unresolved. Many linguists suggest that the use of 个 gè as a noun classifier is arbitrary. This view is challenged in the current study. Relying on the CCL-Corpus of Peking University and data from Google, we investigated which nouns for living beings are most likely classified by the general clas-sifier 个 gè. The results suggest that the use of the classifier 个 gè is motivated by an anthropocentric continuum as described by Köpcke and Zubin in the 199
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8

Lee, Yuchun. "Classifiers : adaptive modules in pattern recognition systems." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14496.

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9

Chungfat, Neil C. (Neil Caye) 1979. "Context-aware activity recognition using TAN classifiers." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87220.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.<br>Includes bibliographical references (p. 73-77).<br>by Neil C. Chungfat.<br>M.Eng.
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10

Li, Ming. "Sequence and text classification : features and classifiers." Thesis, University of East Anglia, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426966.

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