Relevant bibliographies by topics / Unsupervised categorization

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Unsupervised categorization'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Unsupervised categorization"

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Pothos, Emmanuel M., and Nick Chater. "Unsupervised Categorization and Category Learning." Quarterly Journal of Experimental Psychology Section A 58, no. 4 (May 2005): 733–52. http://dx.doi.org/10.1080/02724980443000322.

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When people categorize a set of items in a certain way they often change their perceptions for these items so that they become more compatible with the learned categorization. In two experiments we examined whether such changes are extensive enough to change the unsupervised categorization for the items—that is, the categorization of the items that is considered more intuitive or natural without any learning. In Experiment 1 we directly employed an unsupervised categorization task; in Experiment 2 we collected similarity ratings for the items and inferred unsupervised categorizations using Pot
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Heidemann, Gunther. "Unsupervised image categorization." Image and Vision Computing 23, no. 10 (September 2005): 861–76. http://dx.doi.org/10.1016/j.imavis.2005.05.016.

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Clapper, John P., and Gordon H. Bower. "Adaptive categorization in unsupervised learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 28, no. 5 (September 2002): 908–23. http://dx.doi.org/10.1037/0278-7393.28.5.908.

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Wang, Xiaozhe, Liang Wang, Anthony Wirth, and Leonardo Lopes. "Unsupervised categorization of human motion sequences." Intelligent Data Analysis 17, no. 6 (November 6, 2013): 1057–74. http://dx.doi.org/10.3233/ida-130620.

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Yuchi Huang, Qingshan Liu, Fengjun Lv, Yihong Gong, and Dimitris N. Metaxas. "Unsupervised Image Categorization by Hypergraph Partition." IEEE Transactions on Pattern Analysis and Machine Intelligence 33, no. 6 (June 2011): 1266–73. http://dx.doi.org/10.1109/tpami.2011.25.

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Dolgikh, Serge. "Categorization in Unsupervised Generative Selflearning Systems." International Journal of Modern Education and Computer Science 13, no. 3 (June 8, 2021): 68–78. http://dx.doi.org/10.5815/ijmecs.2021.03.06.

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Gliozzo, Alfio, Carlo Strapparava, and Ido Dagan. "Improving text categorization bootstrapping via unsupervised learning." ACM Transactions on Speech and Language Processing 6, no. 1 (October 2009): 1–24. http://dx.doi.org/10.1145/1596515.1596516.

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Pothos, Emmanuel M., and Nick Chater. "A simplicity principle in unsupervised human categorization." Cognitive Science 26, no. 3 (May 2002): 303–43. http://dx.doi.org/10.1207/s15516709cog2603_6.

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YANG, SHICAI, GEORGE BEBIS, MUHAMMAD HUSSAIN, GHULAM MUHAMMAD, and ANWAR M. MIRZA. "UNSUPERVISED DISCOVERY OF VISUAL FACE CATEGORIES." International Journal on Artificial Intelligence Tools 22, no. 01 (February 2013): 1250029. http://dx.doi.org/10.1142/s0218213012500297.

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Human faces can be arranged into different face categories using information from common visual cues such as gender, ethnicity, and age. It has been demonstrated that using face categorization as a precursor step to face recognition improves recognition rates and leads to more graceful errors. Although face categorization using common visual cues yields meaningful face categories, developing accurate and robust gender, ethnicity, and age categorizers is a challenging issue. Moreover, it limits the overall number of possible face categories and, in practice, yields unbalanced face categories wh
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Ell, Shawn W., and F. Gregory Ashby. "The impact of category separation on unsupervised categorization." Attention, Perception, & Psychophysics 74, no. 2 (November 9, 2011): 466–75. http://dx.doi.org/10.3758/s13414-011-0238-z.

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Dissertations / Theses on the topic "Unsupervised categorization"

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Colreavy, Erin Patricia. "Unsupervised categorization : perceptual shift, strategy development, and general principles." University of Western Australia. School of Psychology, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0232.

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Unsupervised categorization is the task of classifying novel stimuli without external feedback or guidance, and is important for every day decisions such as deciding whether emails fall into 'interesting
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Doan, Charles A. "Connecting Unsupervised and Supervised Categorization Behavior from a Parainformative Perspective." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1521548439515138.

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Miller, Benjamin Alan. "Distance Effects in Similarity Based Free Categorization." CSUSB ScholarWorks, 2015. https://scholarworks.lib.csusb.edu/etd/238.

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This experiment investigated the processes underlying similarity-based free categorization. Of particular interest was how temporal distance between similar objects affects the likelihood that people will put them into the same novel category. Participants engaged in a free categorization task referred to as binomial labeling. This task required participants to generate a two-part label (A1, B1, C1, etc.) indicating family (superordinate) and species (subordinate) levels of categorization for each object in a visual display. Participants were shown the objects one at a time in a sequential pre
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Huckle, Christopher Cedric. "Unsupervised categorization of word meanings using statistical and neural network methods." Thesis, University of Edinburgh, 1996. http://hdl.handle.net/1842/21308.

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A statistical technique is introduced for representing the contexts in which words occur. Each word is represented by a 'statistical context vector', and the vectors are subjected to hierarchical cluster analysis to produce a structure in which words which have similar contexts are placed closer together than those which do not. Analyses of this type are carried out on a 10,000,000 word corpus, using a variety of different parameters, and the appropriateness of the resulting structures is assessed using Roget's Thesaurus as a benchmark. A still more attractive approach is one which deals with
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Pereira, Dennis V. "Automatic Lexicon Generation for Unsupervised Part-of-Speech Tagging Using Only Unannotated Text." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/10094.

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With the growing number of textual resources available, the ability to understand them becomes critical. An essential first step in understanding these sources is the ability to identify the parts-of-speech in each sentence. The goal of this research is to propose, improve, and implement an algorithm capable of finding terms (words in a corpus) that are used in similar ways--a term categorizer. Such a term categorizer can be used to find a particular part-of-speech, i.e. nouns in a corpus, and generate a lexicon. The proposed work is not dependent on any external sources of information, such a
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Liliemark, Adam, and Viktor Enghed. "Categorization of Customer Reviews Using Natural Language Processing." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299882.

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Databases of user generated data can quickly become unmanageable. Klarna faced this issue, with a database of around 700,000 customer reviews. Ideally, the database would be cleaned of uninteresting reviews and the remaining reviews categorized. Without knowing what categories might emerge, the idea was to use an unsupervised clustering algorithm to find categories. This thesis describes the work carried out to solve this problem, and proposes a solution for Klarna that involves artificial neural networks rather than unsupervised clustering. The implementation done by us is able to categorize re
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Tsai, Cheng-han, and 蔡承翰. "Unsupervised Text Categorization Method Using Wikipedia Content and Linking Information." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/25779286117141393805.

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碩士<br>國立雲林科技大學<br>資訊管理系碩士班<br>101<br>With the rapid growth of the Internet, huge amount of text documents has been generated. How to classify the huge quantity of text documents into correct categories is a complex task. Scores of supervised learning algorithms have been proposed for text categorization. However, the supervised learning algorithms have some weaknesses. One of them is that they need a large number of labeled training documents for computing term similarity in order to obtain high accuracy performance. Generally, collecting labeled documents is difficult and costly. In this pape
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"Types of Bots: Categorization of Accounts Using Unsupervised Machine Learning." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55528.

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abstract: Social media bot detection has been a signature challenge in recent years in online social networks. Many scholars agree that the bot detection problem has become an "arms race" between malicious actors, who seek to create bots to influence opinion on these networks, and the social media platforms to remove these accounts. Despite this acknowledged issue, bot presence continues to remain on social media networks. So, it has now become necessary to monitor different bots over time to identify changes in their activities or domain. Since monitoring individual accounts is not feasible,
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Chang, Chun-Chih, and 張駿志. "Enhance Performance of Unsupervised Text Categorization by Using External Information." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/60800446714501803893.

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碩士<br>國立雲林科技大學<br>資訊管理系<br>102<br>With swift growth of online text, how to organize text data effectively has become a major issue. Text classification is the task of classifying documents into pre-defined categories. For this, many supervised classification methods have been proposed. But supervised learning methods have some disadvantage. The biggest bottleneck is the requirement of a large amount of training data for better classification performance. While unlabeled documents are simply collected and abundant, labeled documents are difficult to collect because labeling is usually done manu
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Book chapters on the topic "Unsupervised categorization"

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Saux, Bertrand Le, and Nozha Boujemaa. "Unsupervised Categorization for Image Database Overview." In Recent Advances in Visual Information Systems, 163–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45925-1_15.

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Li, Keqian, Hanwen Zha, Yu Su, and Xifeng Yan. "Unsupervised Neural Categorization for Scientific Publications." In Proceedings of the 2018 SIAM International Conference on Data Mining, 37–45. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2018. http://dx.doi.org/10.1137/1.9781611975321.5.

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Dai, Dengxin, Mukta Prasad, Christian Leistner, and Luc Van Gool. "Ensemble Partitioning for Unsupervised Image Categorization." In Computer Vision – ECCV 2012, 483–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33712-3_35.

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Dolgikh, Serge. "On Unsupervised Categorization in Deep Autoencoder Models." In Advances in Computer Science for Engineering and Education III, 255–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55506-1_23.

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Levine, Daniel S. "Models of Coding, Categorization, and Unsupervised Learning." In Introduction to Neural and Cognitive Modeling, 216–49. Third edition. | New York, NY : Routledge, 2019.: Routledge, 2018. http://dx.doi.org/10.4324/9780429448805-7.

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Tao, Linmi, and Atif Mughees. "Unsupervised Hyperspectral Image Noise Reduction and Band Categorization." In Engineering Applications of Computational Methods, 35–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4420-4_3.

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Shin, Jiwon, Rudolph Triebel, and Roland Siegwart. "Unsupervised 3D Object Discovery and Categorization for Mobile Robots." In Springer Tracts in Advanced Robotics, 61–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29363-9_4.

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Mesnil, Grégoire, Salah Rifai, Antoine Bordes, Xavier Glorot, Yoshua Bengio, and Pascal Vincent. "Unsupervised Learning of Semantics of Object Detections for Scene Categorization." In Advances in Intelligent Systems and Computing, 209–24. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12610-4_13.

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Klami, Mikaela, and Krista Lagus. "Unsupervised Word Categorization Using Self-Organizing Maps and Automatically Extracted Morphs." In Intelligent Data Engineering and Automated Learning – IDEAL 2006, 912–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875581_109.

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Lu, Zhiwu, Xiaoqing Lu, and Zhiyuan Ye. "Unsupervised Image Categorization Using Constrained Entropy-Regularized Likelihood Learning with Pairwise Constraints." In Advances in Neural Networks – ISNN 2007, 1193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72383-7_139.

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Conference papers on the topic "Unsupervised categorization"

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Escobar-Avila, Javier, Mario Linares-Vasquez, and Sonia Haiduc. "Unsupervised Software Categorization Using Bytecode." In 2015 IEEE 23rd International Conference on Program Comprehension (ICPC). IEEE, 2015. http://dx.doi.org/10.1109/icpc.2015.33.

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Limsettho, Nachai, Hideaki Hata, Akito Monden, and Kenichi Matsumoto. "Automatic Unsupervised Bug Report Categorization." In 2014 6th International Workshop on Empirical Software Engineering in Practice (IWESEP). IEEE, 2014. http://dx.doi.org/10.1109/iwesep.2014.8.

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Ko, Youngjoong, and Jungyun Seo. "Automatic text categorization by unsupervised learning." In the 18th conference. Morristown, NJ, USA: Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/990820.990886.

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Wetzker, Robert, Tansu Alpcan, Christian Bauckhage, Winfried Umbrath, and Sahin Albayrak. "An unsupervised hierarchical approach to document categorization." In IEEE/WIC/ACM International Conference on Web Intelligence (WI'07). IEEE, 2007. http://dx.doi.org/10.1109/wi.2007.144.

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Yang, Jie, Zhenjiang Miao, and Hao Wu. "Unsupervised image categorization with improved spectral clustering." In 2014 12th International Conference on Signal Processing (ICSP 2014). IEEE, 2014. http://dx.doi.org/10.1109/icosp.2014.7015226.

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Irfan, Danish, Xu Xiaofei, Deng Shengchun, and Ye Yunming. "Feature-based unsupervised clustering for supplier categorization." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630655.

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Fleming, M. K., and G. W. Cottrell. "Categorization of faces using unsupervised feature extraction." In 1990 IJCNN International Joint Conference on Neural Networks. IEEE, 1990. http://dx.doi.org/10.1109/ijcnn.1990.137696.

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Gliozzo, Alfio, Carlo Strapparava, and Ido Dagan. "Investigating unsupervised learning for text categorization bootstrapping." In the conference. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1220575.1220592.

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Diemert, Eustache, and Gilles Vandelle. "Unsupervised query categorization using automatically-built concept graphs." In the 18th international conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1526709.1526772.

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Dueck, Delbert, and Brendan J. Frey. "Non-metric affinity propagation for unsupervised image categorization." In 2007 IEEE 11th International Conference on Computer Vision. IEEE, 2007. http://dx.doi.org/10.1109/iccv.2007.4408853.

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