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Relevant bibliographies by topics / Category learning

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Academic literature on the topic 'Category learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Category learning"

1

Markman, Arthur B., and Brian H. Ross. "Category use and category learning." Psychological Bulletin 129, no. 4 (2003): 592–613. http://dx.doi.org/10.1037/0033-2909.129.4.592.

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Kersten, Alan W., and Dorrit Billman. "Event category learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 23, no. 3 (1997): 638–58. http://dx.doi.org/10.1037/0278-7393.23.3.638.

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Ashby, F. Gregory, and W. Todd Maddox. "Human Category Learning." Annual Review of Psychology 56, no. 1 (2005): 149–78. http://dx.doi.org/10.1146/annurev.psych.56.091103.070217.

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Paradis, Rosemary D., Jinhong K. Guo, John Olden-Stahl, and Jack Moulton. "Cognitive Category Learning." Procedia Computer Science 12 (2012): 188–93. http://dx.doi.org/10.1016/j.procs.2012.09.052.

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Pollmann, Stefan. "Sensory category learning." Nature Human Behaviour 2, no. 7 (2018): 448–49. http://dx.doi.org/10.1038/s41562-018-0377-5.

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Richler, Jennifer J., and Thomas J. Palmeri. "Visual category learning." Wiley Interdisciplinary Reviews: Cognitive Science 5, no. 1 (2013): 75–94. http://dx.doi.org/10.1002/wcs.1268.

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Heffner, Christopher C. "Age and category structure in phonetic category learning." Journal of the Acoustical Society of America 155, no. 3_Supplement (2024): A269. http://dx.doi.org/10.1121/10.0027460.

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When learning new speech sound categories in a second language, listeners must decide which sounds belong to the same category and which belong to different categories. This learning process is contingent on perceptual systems that change across the lifespan, which may, in turn, make certain categories easier to learn depending on which systems are employed by learners. In the present study, learners across a variety of ages categorize speech sounds taken from German that vary in their category structure (i.e., the complexity of the categories being learned within the stimulus space). Particip

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Ward, Thomas B. "Whenis category learning holistic?" Memory & Cognition 16, no. 1 (1988): 85–89. http://dx.doi.org/10.3758/bf03197749.

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Holt, Lori L., Yafit Gabay, Frederic Dick, and Jason Zevin. "Incidental auditory category learning." Journal of the Acoustical Society of America 137, no. 4 (2015): 2386. http://dx.doi.org/10.1121/1.4920684.

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Goldstone, Robert L. "Unitization during category learning." Journal of Experimental Psychology: Human Perception and Performance 26, no. 1 (2000): 86–112. http://dx.doi.org/10.1037/0096-1523.26.1.86.

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Dissertations / Theses on the topic "Category learning"

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

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

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

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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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Books on the topic "Category learning"

1

Peng, Lin. Investor attention: Overconfidence and category learning. National Bureau of Economic Research, 2005.

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Whitfield, Wendy. Learning by achievement: Ten projects funded by category xix education support grants. Youth Work Press, 1991.

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Voiklis, John K. A Thing Is What We Say It Is: Referential Communication and Indirect Category Learning. [publisher not identified], 2008.

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Savatovsky, Dan, Mariangela Albano, Thị Kiều Ly Phạm, and Valérie Spaëth. Language Learning and Teaching in Missionary and Colonial Contexts. Amsterdam University Press, 2023. http://dx.doi.org/10.5117/9789463728249.

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This volume assembles texts dedicated to the linguistic and educational aspects of missionary and colonial enterprises, taking into account all continents and with an extended diachronic perspective (15th–20th centuries). Strictly speaking, this “linguistics” is contemporary to the colonial era, so it is primarily the work of missionaries of Catholic orders and Protestant societies. It can also belong to a retrospective outlook, following decolonization. In the first category, one mostly finds transcription, translation, and grammatization practices (typically, the production of dictionaries a

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Karpikov, Aleksey, and Sergey Kondrat'ev. Psychology of learning and education: the Christian humanitarian paradigm. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/25286.

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The monograph deals with the issues of spiritual, moral and intellectual development of the individual in terms of training and education from a position of humanitarian Christian psychological paradigm. Defined methodological basis of Christian psychology education, and its subject, tasks and basic categories; from the position of metaphysical and empirical levels of explanation of the theory of identity as a core category of psychology of education, proposed the concept of intelligence as a core category of psychology training. From the standpoint of Christian psychology education reve

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Song, Qiuying. Quan qiu hua bei jing xia Zhongguo jiao xue lun ben tu hua wen ti yan jiu: Yi jiao yu xue de guan xi fan chou wei ji dian = The research on didactic localization in China under the background of globalization : on the basis of teaching-learning relational category. Ke xue chu ban she, 2019.

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Biba, Anna, and Galina Razumova. Teaching Russian and developing meta-subject skills of students in inclusive primary education classes. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1871394.

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The monograph attempts to identify and substantiate the content and complex of spelling teaching tools for younger schoolchildren with a pronounced general underdevelopment of speech with the simultaneous development of their academic independence. To solve the problem, both theoretical research methods (analysis of psychological and pedagogical literature, generalization of scientific data) and empirical (observation, conversation, testing, analysis of student activity products) were used. The theoretical basis of teaching and development of students with mild speech underdevelopment in inclu

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Leritz, Lyle E. Analytic and non-analytic category learning. 2000.

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Kruschke, John Kendall. A connectionist model of category learning. 1990.

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What's That Category? a Book for Learning Basic and Elementary Level Categories: 40 Full Color Photos for Visual Learners. Independently Published, 2020.

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Book chapters on the topic "Category learning"

1

Shultz, Thomas R., Scott E. Fahlman, Susan Craw, et al. "Category." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_101.

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Liu, Mark. "Multi-Category Image Classifications." In Machine Learning, Animated. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/b23383-9.

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Ellis, Nick C. "2. Construction learning as category learning." In Cognitive Processing in Second Language Acquisition. John Benjamins Publishing Company, 2010. http://dx.doi.org/10.1075/celcr.13.05ell.

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Crammer, Koby, and Yoram Singer. "Loss Bounds for Online Category Ranking." In Learning Theory. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_4.

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Huyck, Christian. "Quasi Biologically Plausible Category Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77915-2_3.

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Fortnow, Lance, RŪsiņš Freivalds, William I. Gasarch, et al. "Measure, category and learning theory." In Automata, Languages and Programming. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60084-1_105.

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Grauman, Kristen, and Bastian Leibe. "Learning Generic Object Category Models." In Visual Object Recognition. Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_10.

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Heit, Evan, and Lewis Bott. "Selecting Knowledge for Category Learning." In Proceedings of the Twenty First Annual Conference of the Cognitive Science Society. Psychology Press, 2020. http://dx.doi.org/10.4324/9781410603494-43.

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Love, Bradley C., and Douglas L. Medin. "Modeling Item and Category Learning." In Proceedings of the Twentieth Annual Conference of the Cognitive Science Society. Routledge, 2022. http://dx.doi.org/10.4324/9781315782416-119.

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He, Wang, Peng Liu, and Qian Qian. "Case Study: Elo Merchant Category Recommendation." In Machine Learning Contests: A Guidebook. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3723-3_8.

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Conference papers on the topic "Category learning"

1

zhou, jie. "Deep learning in food category recognition." In 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), edited by Bin Liu and Lu Leng. SPIE, 2024. http://dx.doi.org/10.1117/12.3050535.

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Wang, Yu, Bingchen Zhao, Yongchun Lu, Guoqiang Xiao, and Quan Lu. "Debiased Prototypical Learning Improves Generalized Category Discovery." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687913.

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Choi, Sua, Dahyun Kang, and Minsu Cho. "Contrastive Mean-Shift Learning for Generalized Category Discovery." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02179.

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Luo, Tingzhang, Mingxuan Du, Jiatao Shi, Xinxiang Chen, Bingchen Zhao, and Shaoguang Huang. "Contextuality Helps Representation Learning for Generalized Category Discovery." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647298.

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Peng, Wanli, Qiang Liu, and Yi Sun. "Category-Level Object Pose Estimation with Uncertainty Learning." In 2024 IEEE 14th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2024. http://dx.doi.org/10.1109/cyber63482.2024.10749174.

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Guo, Qianhang, Kunyuan Xu, Haoyu Huang, and Qiongxiong Ma. "Learning From Category-attribute Label for Image Attribute Recognition." In 2025 4th Asia Conference on Algorithms, Computing and Machine Learning (CACML). IEEE, 2025. https://doi.org/10.1109/cacml64929.2025.11010960.

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Sun, Jifeng. "Self-supervised learning based on spatial scale learning and category prediction." In International Conference on Optics, Electronics, and Communication Engineering, edited by Yang Yue. SPIE, 2024. http://dx.doi.org/10.1117/12.3049840.

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Agalayil, Helen Terese, Radha D., and V. S. Kirthika Devi. "Smartphone Price Prediction and Category Classification Using Machine Learning." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914919.

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Garg, Swati, Chandra Sekhar, and Lov Kumar. "Unlocking Potential: A Machine Learning Approach to Job Category Prediction." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752119.

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Sommer, Leonhard, Artur Jesslen, Eddy Ilg, and Adam Kortylewski. "Unsupervised Learning of Category-Level 3D Pose from Object-Centric Videos." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02150.

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Reports on the topic "Category learning"

1

Peng, Lin, and Wei Xiong. Investor Attention: Overconfidence and Category Learning. National Bureau of Economic Research, 2005. http://dx.doi.org/10.3386/w11400.

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Love, Bradley C. Category Learning by Clustering with Extension to Dynamic Environments. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada546608.

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Love, Bradley C. Category Learning by Clustering with Extension to Dynamic Environments. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada547256.

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Filoteo, J. V., Erick J. Paul, F. G. Ashby, et al. Simulating Category Learning and Set Shifting Deficits in Patients Weight-Restored from Anorexia Nervosa. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada597795.

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Schipper, Youdi, Isaac Mbiti, and Mauricio Romero. Designing and Testing a Scalable Teacher Incentive Programme in Tanzania. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-ri_2022/044.

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School participation in Tanzania has increased dramatically over the past two decades: primary school enrolment increased from 4.9 million in 2001 to 10.9 million in 2020. While 81 percent of primary-school-age children are currently enrolled, over the last ten years, the primary completion rate has dropped and remains below 70 percent since 2015 (data from UNESCO Institute for Statistics).1 Despite improvements in enrolment, indicators of foundational learning remain low. According to the 2020 report of the Standard Two National Assessment (STNA), conducted by the National Examinations Counci

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Karki, Shanta, Marina Apgar, Mieke Snijder, and Ranjana Sharma. Learning from Life Story Collection and Analysis With Children Who Work in the Worst Forms of Child Labour in Nepal. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/clarissa.2022.007.

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The CLARISSA Nepal team collected and analysed 400 life stories of children and young people engaged in or affected by the worst forms of child labour (WFCL), particularly in the “Adult Entertainment” sector in Nepal, which includes children working in Dohoris (restaurants playing folk music), dance bars, spa-massage parlours, khaja ghars (tea/snack shop) and guest houses. Stories were also collected from children in CLARISSA’s focus neighbourhoods, children in this category include street connected children and those working in transportation, party palaces, domestic labour and construction s

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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/41302.

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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR

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Chew, Robert F., Kirsty J. Weitzel, Peter Baumgartner, et al. Improving Text Classification with Boolean Retrieval for Rare Categories: A Case Study Identifying Firearm Violence Conversations in the Crisis Text Line Database. RTI Press, 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0050.2304.

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Advancements in machine learning and natural language processing have made text classification increasingly attractive for information retrieval. However, developing text classifiers is challenging when no prior labeled data are available for a rare category of interest. Finding instances of the rare class using a uniform random sample can be inefficient and costly due to the rare category’s low base rate. This work presents an approach that combines the strengths of text classification and Boolean retrieval to help learn rare concepts of interest. As a motivating example, we use the task of f

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Heckman, Stuart. Understanding insurance decisions: A review of risk management decision making, risk literacy, and racial/ethnic differences. Center for Insurance Policy and Research, 2024. http://dx.doi.org/10.52227/26712.2024.

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The racial/ethnic wealth gap is a stunning feature of U.S. household finances. Although the causes of the gap are complex, it is important that researchers investigate disparities between racial/ethnic groups in household financial management areas. We posit that first understanding insurance decisions as a critical component of overall household financial management is an important avenue for further understanding factors that may perpetuate or reduce the racial wealth gap. Moreover, risk management, including the purchase and use of insurance products, is a key yet challenging area for house

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