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Dissertations / Theses on the topic 'Category learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Richardson, G. P. "Category names and category learning." Thesis, University of Stirling, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306826.

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2

McGuire, Grant Leese. "Phonetic category learning." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1190065715.

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3

Kersten, Alan Wayne. "Event category learning." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/30882.

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4

McGuire, Grant L. "Phonetic category learning." The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1190065715.

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5

Cagigas, Xavier E. "Cultural determinants of category learning." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3307160.

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Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2008.<br>Title from first page of PDF file (viewed July 9, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 105-118).

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6

Bartos, Paul D. "Connectionist modelling of category learning." Thesis, Open University, 2002. http://oro.open.ac.uk/18865/.

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A shortcoming is identified with respect to the ability of exemplar-based connectionist models of category learning to offer accounts of learning about stimuli with variable dimensionality. Models which may simulate these tasks, such as the configural-cue network (Gluck & Bower, 1988b), appear to be unable to accurately simulate certain data well simulated by exemplar-based models such as ALCOVE (Kruschke, 1992). A task in which the advantage of ALCOVE is exemplified is the prediction of human learning rates on the six category structures tested by Shepard, Hovland, and Jenkins (1961). The abi

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7

Martin, Sean. "Bias in Novel Category Learning." Thesis, New York University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=3740789.

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<p> This dissertation explores the interaction of statistical learning and bias. While novel category learning is known to involve statistical learning mechanisms, there is clear evidence that learners bring bias of various forms into the process, resulting in learning patterns that differ from the expected outcome of purely distributional learning. Here, I explore the way that the learner's experienced-based expectations about the informativeness of acoustic cues interacts with the informativeness of those cues during novel category learning. This question is addressed using a combination of

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8

Bartos, Paul D. "Connectionist modelling of category learning." n.p, 2001. http://dart.open.ac.uk/abstracts/page.php?thesisid=155.

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9

Shikano, Teruyuki. "Effect of instructions in category learning." Thesis, Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/30966.

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10

Aytar, Yusuf. "Transfer learning for object category detection." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:c9e18ff9-df43-4f67-b8ac-28c3fdfa584b.

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Object category detection, the task of determining if one or more instances of a category are present in an image with their corresponding locations, is one of the fundamental problems of computer vision. The task is very challenging because of the large variations in imaged object appearance, particularly due to the changes in viewpoint, illumination and intra-class variance. Although successful solutions exist for learning object category detectors, they require massive amounts of training data. Transfer learning builds upon previously acquired knowledge and thus reduces training requirement

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Barcus, Karina-Mikayla C. "Auditory Category Learning of Modal Concepts." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1428416128.

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12

Xu, Yang. "Cortical spatiotemporal plasticity in visual category learning." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/272.

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Central to human intelligence, visual categorization is a skill that is both remarkably fast and accurate. Although there have been numerous studies in primates regarding how information flows in inferiortemporal (ITC) and prefrontal (PFC) cortices during online discrimination of visual categories, there has been little comparable research on the human cortex. To bridge this gap, this thesis explores how visual categories emerge in prefrontal cortex and the ventral stream, which is the human homologue of ITC. In particular, cortical spatiotemporal plasticity in visual category learning was inv

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13

Wilson, Rachel. "Syntactic category learning in a second language." Diss., The University of Arizona, 2002. http://hdl.handle.net/10150/289805.

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A central question in the study of language learning is how humans acquire syntactic categorical distinctions among words (e.g. noun, verb, etc.). Past research using miniature artificial grammars suggests that semantic information is not needed for this learning; distributional information alone can provide adequate input for learning. The current experiments extended this finding to a natural language. Adults who had never studied Russian listened to lists of Russian words for seven minutes. The words consisted of a content morpheme and a grammatical ending. The participants were not told th

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Gupta, Ashish. "Learning a structured model for visual category recognition." Thesis, University of Surrey, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.600016.

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This thesis deals with the problem of estimating structure in data due to the semantic relations between data. elements and leveraging this information to learn a visual model for category recognition. A visual model consists of dictionary learning, which computes a succinct set of prototypes from training data by partitioning feature space, and feature encoding, which learns a representation of each image as a combination of dictionary elements. Besides 'variations in lighting and pose, a key challenge of classifying a category is intra-category appearance variation. The key idea. in this the

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15

Wichrowska, Olga N. "Unsupervised syntactic category learning from child-directed speech." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62756.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 57-59).<br>The goal of this research was to discover what kinds of syntactic categories can be learned using distributional analysis on linear context of words, specifically in child-directed speech. The idea behind this is that the categories used by children could very well be different from adult categories. There is some evidence that distributional analysis could be used for some aspects of

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16

Weatherholtz, Kodi. "Perceptual learning of systemic cross-category vowel variation." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429782580.

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17

Heffernan, Megan Mary Psychology Faculty of Science UNSW. "An examination of the processes underlying probabilistic category learning." Awarded By:University of New South Wales. Psychology, 2010. http://handle.unsw.edu.au/1959.4/44760.

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This thesis examined the role of procedural learning in human probabilistic category learning (PCL). It was proposed that there was a lack of clear behavioural evidence for learning without awareness in PCL. Eleven experiments are reported that investigated the characteristics of learning in a prototypical probabilistic category learning task (the weather prediction task). The results were interpreted as contradicting the popular interpretation of weather prediction task learning as procedurally based. Rather, it was shown that behavioural data was consistent with declarative learning. This le

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18

Remick, Olga V. "Learning to attend: Measuring sequential effects of feedback in overt visual attention during category learning." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54397.

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Trial-level evidence for feedback sensitivity in fixations during category learning has been previously described as weak. In this dissertation, steps were taken to overcome some methodological issues potentially obscuring the evidence for such sensitivity. Jointly, the three experiments reported here suggest that sensitivity to error in visual attention reflects cue competition, as opposed to error-driven learning of a selective visual profile. These outcomes are in agreement with previous research in human vision, which holds that fixations reflect the agent’s task representation. A case is

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19

Leung, Dilys Hay Lok. "Infants' use of object category distinctions in word learning." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/33957.

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How do infants initially determine whether a novel object word labels a specific individual (e.g. Madonna) or an instance of a category (e.g., a person)? The research in this dissertation tested the hypothesis that infants assume words for objects from some categories (e.g., people) label individuals (are proper names) but words for objects from other categories (e.g., artifacts) label instances of the category (are count nouns). This assumption could help infants to identify proper names and count nouns in their language, and thereby facilitate the learning of the linguistic proper name/count

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20

Allan, Moray. "Sprite learning and object category recognition using invariant features." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/2430.

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This thesis explores the use of invariant features for learning sprites from image sequences, and for recognising object categories in images. A popular framework for the interpretation of image sequences is the layers or sprite model of e.g. Wang and Adelson (1994), Irani et al. (1994). Jojic and Frey (2001) provide a generative probabilistic model framework for this task, but their algorithm is slow as it needs to search over discretised transformations (e.g. translations, or affines) for each layer. We show that by using invariant features (e.g. Lowe’s SIFT features) and clustering their mo

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21

Edmunds, C. E. R. "Critique of a dual-system model of category learning." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/8817.

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Some researchers have argued that the category learning literature is conclusive: people learn to generalise from past experiences to novel ones using multiple learning systems (Ashby & Maddox, 2011). To the extent that this claim is true, it is due in no small part to work investigating the predictions of the dual-system model COVIS (COmpetition between Verbal and Implicit Systems; Ashby, Alfonso-Reese, Turken, & Waldron, 1998). The work presented here investigates the evidence for this model. In Chapter 1, I describe the main features of the COVIS model and briefly review some of the evidenc

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22

Carlson, Krista D. "The Impact of Working Memory Capacity on Category Learning." Miami University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=miami1260204364.

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23

Morehead, Kayla Elizabeth Morehead. "Sources of Individual Differences in Self-regulated Category Learning." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1563456095222779.

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24

Venkataramanan, Shashanka. "Metric learning for instance and category-level visual representation." Electronic Thesis or Diss., Université de Rennes (2023-....), 2024. http://www.theses.fr/2024URENS022.

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Le principal objectif de la vision par ordinateur est de permettre aux machines d'extraire des informations significatives à partir de données visuelles, telles que des images et des vidéos, et de tirer parti de ces informations pour effectuer une large gamme de tâches. À cette fin, de nombreuses recherches se sont concentrées sur le développement de modèles d'apprentissage profond capables de coder des représentations visuelles complètes et robustes. Une stratégie importante dans ce contexte consiste à préentraîner des modèles sur des ensembles de données à grande échelle, tels qu'ImageNet, p

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25

Sewell, David K. "Restructuring partitioned knowledge : evidence of strategy retention in category learning." University of Western Australia. Faculty of Life and Physical Sciences, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0008.

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A recurring theme in the cognitive development literature is the notion that people restructure their task knowledge as they develop increasingly sophisticated strategies. A large body of empirical literature spanning several domains suggests that in some cases, the process of knowledge restructuring is best characterized by a process of sequentially replacing old strategies with newer ones. In other cases, restructuring appears to be better characterized as a process involving changes in the way partial knowledge elements are selectively applied to a task. Critically, the former, but not the

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26

Forbes, Samuel Henry. "Colour word and colour category learning in infants and toddlers." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:8ec923a1-fa95-4610-8c90-594033b2e706.

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This thesis examines how and when infants learn colour words, and how the knowledge of colour words affects their comprehension of colour categories. Over the course of seven experimental chapters, the ability of infants and toddlers to learn colour words, use colour words to process colours, and the role that colour words play in learning to perceive colour are all assessed. Chapters 2 and 3 assess claims that colour words are learned late using parental report and eye-tracking methods, finding that colour words are learned as early as 19 months. In contrast to this, Chapter 4 demonstrates th

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27

Abedin, Reaz Ashraful. "Autonomous Object Category Learning for Service Robots Using Internet Resources." Thesis, Umeå universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-128299.

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With the developments in the field of Artificial Intelligence (AI), robots are becoming smarter, more efficient and capable of doing more dififcult tasks than before. Recent progress in Machine Learning has revolutionized the field of AI. Rather than performing pre-programmed tasks, nowadays robots are learning things, and becoming more autonomous along the way. However, in most of the cases, robots need a certain level of human assistance to learn something. To recognize or classify daily objects is a very important skill that a service robot should possess. In this research work, we have imp

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28

Tharp, Ian James. "The effect of personality on attentional strategy in category learning." Thesis, Goldsmiths College (University of London), 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.499975.

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This thesis explores the mediating effects of personality on attention and performance during the learning of novel categories. Major theories of category learning emphasise the role of dopamine on a variety of processes engaged during such learning. Two core personality domains, namely extraversion and a cluster of traits collectively termed impulsive, anti-social, sensation seeking (ImpASS) were considered These personality traits were of interest because it has been suggested that their biological basis may partly reflect variation in dopaminergic neurotransmission. Schizotypal personality,

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29

Kittur, Aniket. "Feature-based vs. relational category learning a dual process view /." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1456295591&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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30

LaShell, Patrick Jonathon. "What's in a rule two-dimensional rule use in category learning /." Diss., [Riverside, Calif.] : University of California, Riverside, 2010. http://proquest.umi.com/pqdweb?index=0&did=2019837061&SrchMode=2&sid=2&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1274467478&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2009.<br>Includes abstract. Available via ProQuest Digital Dissertations. Title from first page of PDF file (viewed March 23, 2010). Includes bibliographical references. Also issued in print.

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31

MacKendrick, Alex. "Interleaved Effects in Inductive Category Learning: The Role of Memory Retention." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5846.

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Interleaved effects are widely documented. Research demonstrates that interleaved presentation orders, as opposed to blocked orders typically benefit inductive category learning. What drives interleaved effects is less straightforward. Interleaved presentations provide both the opportunity to compare and contrast between different types of category exemplars, which are temporally juxtaposed, and the opportunity to space study of the same type of category exemplars, which are temporally separated within the presentation span. Accordingly, interleaved effects might be driven by enhanced discrimi

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32

Blunt, Carl T. "Representation change and the development of new attributes through category learning." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/31031.

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33

Frermann, Lea. "Bayesian models of category acquisition and meaning development." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/25379.

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The ability to organize concepts (e.g., dog, chair) into efficient mental representations, i.e., categories (e.g., animal, furniture) is a fundamental mechanism which allows humans to perceive, organize, and adapt to their world. Much research has been dedicated to the questions of how categories emerge and how they are represented. Experimental evidence suggests that (i) concepts and categories are represented through sets of features (e.g., dogs bark, chairs are made of wood) which are structured into different types (e.g, behavior, material); (ii) categories and their featural representatio

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Hsu, Alexander Sirui. "Automatic Internet of Things Device Category Identification using Traffic Rates." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/88421.

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Due to the ever increasing supply of new Internet of Things (IoT) devices being added onto a network, it is vital secure the devices from incoming cyber threats. The manufacturing process of creating and developing a new IoT device allows many new companies to come out with their own device. These devices also increase the network risk because many IoT devices are created without proper security implementation. Utilizing traffic patterns as a method of device type detection will allow behavior identification using only Internet Protocol (IP) header information. The network traffic captured fro

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35

Gaitonde, Suchita S. "RULE-BASED CATEGORY LEARNING: AN EFFECTIVE TREATMENT OPTION IN TRAUMATIC BRAIN INJURY." Oxford, Ohio : Miami University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1058296601.

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Thesis (M.A.)--Miami University, Dept. of Speech Pathology and Audiology, 2003.<br>Title from first page of PDF document. Document formatted into pages; contains vii, 66 p. Includes bibliographical references (p. 53-59).

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36

Carpenter, Kathryn Louise. "An examination of the neural correlates and behavioural phenomena of category learning." Thesis, University of Exeter, 2017. http://hdl.handle.net/10871/28194.

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This thesis investigates the neurobiological pathways that underpin learning of visual categories, and the behaviour associated with these neural systems. The work contains two strands. The first assesses the neural and behavioural predictions of the COmpetition between Verbal and Implicit Systems (COVIS) account of category learning. The second aims to examine the brain regions implicated in the prototype effect after transcranial Direct Current Stimulation (tDCS) to the left dorsolateral prefrontal cortex (DLPFC). COVIS predicts there are separate explicit and implicit category learning syst

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37

Curtis, Michael T. "Perceptual judgment the impact of image complexity and training method on category learning." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4749.

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The goal of this dissertation was to bridge the gap between perceptual learning theory and training application. Visual perceptual skill has been a vexing topic in training science for decades. In complex task domains, from aviation to medicine, visual perception is critical to task success. Despite this, little, if any, emphasis is dedicated to developing perceptual skills through training. Much of this may be attributed to the perceived inefficiency of perceptual training. Recent applied research in perceptual training with discrimination training, however, holds promise for improved percept

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Chatfield, Ken. "On-the-fly visual category search in web-scale image collections." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:cb26c472-b253-4fec-a88e-0c57fc9d70e7.

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This thesis tackles the problem of large-scale visual search for categories within large collections of images. Given a textual description of a visual category, such as 'car' or 'person', the objective is to retrieve images containing that category from the corpus quickly and accurately, and without the need for auxiliary meta-data or, crucially and in contrast to previous approaches, expensive pre-training. The general approach to identifying different visual categories within a dataset is to train classifiers over features extracted from a set of training images. The performance of such cla

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39

Musayeva, Khadija. "Generalization Performance of Margin Multi-category Classifiers." Thesis, Université de Lorraine, 2019. http://www.theses.fr/2019LORR0096/document.

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Cette thèse porte sur la théorie de la discrimination multi-classe à marge. Elle a pour cadre la théorie statistique de l’apprentissage de Vapnik et Chervonenkis. L’objectif est d’établir des bornes de généralisation possédant une dépendances explicite au nombre C de catégories, à la taille m de l’échantillon et au paramètre de marge gamma, lorsque la fonction de perte considérée est une fonction de perte à marge possédant la propriété d’être lipschitzienne. La borne de généralisation repose sur la performance empirique du classifieur ainsi que sur sa "capacité". Dans cette thèse, les mesures

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Kazerounian, Sohrob. "A laminar cortical model of conscious speech perception: phonemic restoration and speech category learning." Thesis, Boston University, 2012. https://hdl.handle.net/2144/31574.

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Thesis (Ph.D.)--Boston University<br>PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>How do the laminar circuits of neocortex learn categories that can support conscious percepts of speech and language? How do learned speech categories become selectively tuned to different temporal sequences

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41

Graham, Erin Nicole. "The Role of Implicit Priming in the Acquisition and Processing of Complex Semantic Categories." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1559132722298381.

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42

Hodge, Kevin Abbott. "Transfer of training as a function of semantic relatedness in a category search task." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/28853.

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43

Christenson, Christopher Adam. "Special education eligibility and the learning disability category examining environmental, cultural, and socio-economic factors /." Online access for everyone, 2004. http://www.dissertations.wsu.edu/Dissertations/Fall2004/c%5Fchristenson%5F120804.pdf.

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Musayeva, Khadija. "Generalization Performance of Margin Multi-category Classifiers." Electronic Thesis or Diss., Université de Lorraine, 2019. http://www.theses.fr/2019LORR0096.

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Cette thèse porte sur la théorie de la discrimination multi-classe à marge. Elle a pour cadre la théorie statistique de l’apprentissage de Vapnik et Chervonenkis. L’objectif est d’établir des bornes de généralisation possédant une dépendances explicite au nombre C de catégories, à la taille m de l’échantillon et au paramètre de marge gamma, lorsque la fonction de perte considérée est une fonction de perte à marge possédant la propriété d’être lipschitzienne. La borne de généralisation repose sur la performance empirique du classifieur ainsi que sur sa "capacité". Dans cette thèse, les mesures

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45

Asriel, Melanie Waldrop. "Aging and Selective Attention in Causal Learning." TopSCHOLAR®, 2011. http://digitalcommons.wku.edu/theses/1085.

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This study investigated age differences in generalization of causal value employing similarity as a cue to causality. Exemplars from six food categories (A+, B-, C+, D-. E+, F-) were presented to both young and older adults in two contiguous training phases. Training Phase 1 included exemplars from categories A+, B-, C+, D-. Training Phase 2 included exemplars from A+, B-, E+, F-. Foods in the “+” categories were paired with an outcome of sickness and foods in the “-” categories were not paired with sickness. Tests of causal judgment and exemplar recognition were conducted. For causal judgment

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46

Berg, Mark. "Visual category learning with dimensionally-separable stimuli : a comparison of performance between pigeons and humans." Thesis, University of Canterbury. Psychology, 2010. http://hdl.handle.net/10092/3820.

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Understanding how organisms learn perceptual categories on the basis of experience has been an important goal for researchers in a number of subdisciplines of psychology, including behavior analysis, experimental psychology, and comparative cognition. The primary aim of this thesis is to investigate how nonhumans (pigeons) and humans learn to make visual category judgments when stimuli vary quantitatively along two dimensions, particularly when accurate responding requires integration of information from both dimensions. The thesis consists of four chapters and a technical appendix. Chapter 1

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Feijóo, Antolín Sara. "Learning from the input: syntactic, semantic and phonological cues to the noun category in English." Doctoral thesis, Universitat de Barcelona, 2010. http://hdl.handle.net/10803/673498.

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Las líneas centrales de la teoría lingüística de los últimos años han descrito la adquisición y el desarrollo de primeras lenguas como un proceso basado en el conocimiento innato de las propiedades gramaticales de la lengua por parte de los niños aprendices. Tales descripciones del desarrollo sintáctico asumen que el entorno lingüístico al que los niños están expuestos es demasiado limitado como para dar lugar al aprendizaje de una lengua (es decir, el llamado argumento de la pobreza del estímulo). Así pues, según esta visión, es el conocimiento lingüístico innato lo que permite a los niños ll

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48

Lee, Mark D. "The effects of inconsistency on the maintenance of skill level in a semantic category search task." Thesis, Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/28732.

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49

Mauldin, Kristin Noel. "EXAMINATION OF THE NEURAL CORRELATES UNDERLYING MULTIPLE-EXEMPLAR CATEGORY LEARNING IN BILATERAL RABBIT EYEBLINK CLASSICAL CONDITIONING." Miami University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=miami1190854129.

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Dʹavila, David Michael. "The consistency bias and categorization : the effects of consistent contrast and hierarchical organization on category learning and transfer." Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/28919.

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