Relevant bibliographies by topics / Unsupervised categorization

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  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 Pothos and Chater's (2002) model of unsupervised categorization. The unsupervised categorization for the items changed to resemble more the learned one when this was specified by the suppression of a stimulus dimension (both experiments), but less so when it was almost specified by the suppression of a stimulus dimension (Experiment 1, nonsignificant trend in Experiment 2). By contrast, no changes in the unsupervised categorization were observed when participants were taught a classification that was specified by a more fine tuning of the relative salience of the two dimensions.
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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 which can compromise recognition performance. This paper investigates ways to automatically discover a categorization of human faces from a collection of unlabeled face images without relying on predefined visual cues. Specifically, given a set of face images from a group of known individuals (i.e., gallery set), our goal is finding ways to robustly partition the gallery set (i.e., face categories). The objective is being able to assign novel images of the same individuals (i.e., query set) to the correct face category with high accuracy and robustness. To address the issue of face category discovery, we represent faces using local features and apply unsupervised learning (i.e., clustering). To categorize faces in novel images, we employ nearest-neighbor algorithms or learn the separating boundaries between face categories using supervised learning (i.e., classification). To improve face categorization robustness, we allow face categories to share local features as well as to overlap. We demonstrate the performance of the proposed approach through extensive experiments and comparisons using the FERET database.
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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 presentation; after labeling each object, they were asked to describe the similarity between that object and previous objects by selecting one of five choices from a drop down menu. Our main prediction was that temporal distance should affect categorization, specifically, that people should be less likely to give two identical objects the same category label the farther apart they are shown in the display. The primary question being addressed in this study was whether the effects of distance are due to a decreased likelihood of remembering the first object when labeling the second (what we refer to as a stage 1 or sampling effect) or to factors during the actual comparison itself (a stage 2 or decision effect)? Our results showed a significant effect of distance on both the likelihood of giving identical objects the same label as well as on the likelihood of mentioning the first object when labeling the second object in an identical pair. Specifically, as the distance between two identical objects increased, the likelihood of giving them the same label, as well as mentioning their similarity, both decreased. Importantly, the decreased probability of giving the second object the same label seemed entirely due to the decreased probability of remembering (sampling) the first object, as indicated by the menu responses. These results provide strong support for the idea that the effect of temporal distance on free categorization is mainly due to stage 1 factors, specifically to its effect on the availability of the first instance in memory when labeling the second. No strong evidence was found in this experiment supporting a separate distance effect at the comparison-decision stage (i.e., stage 2).
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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 polysemy, and which develops its representations for word meanings continuously from the outset, with no need for a separate stage of statistical analysis. To take these consideration into account, an unsupervised neural network is presented, in which different senses of a word token are assigned to different output clusters as the contexts of their occurrence dictate. After initial testing using Elman's (1988) artificial corpus, the network's performance is assessed on the 10,000,000 word corpus by comparing the ways in which different word tokens are distributed over the output units. Further analyses are carried out in which a crude measure of this distribution is assessed using Jones' (1985) 'Ease of Predication' measure. Ease of Predication is found to account for a significant amount of the variance in the distribution measure. Word frequency is also found to play a significant role, and word frequency effects are reassessed in the light of this. The psychological implications of the results obtained from the network are discussed. It is concluded that there is a great deal of information inherent in the structure of language which could potentially play an important part in developing a conceptual structure for word meanings. Whilst extralinguistic information is undoubtedly likely to be of importance as well, it is striking that the use of very simple statistical measures can permit the development of such rich structures.
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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 as dictionaries, and it shows a significant improvement (~30%) over an existing method of categorization. More importantly, the proposed algorithm can be applied as a component of an unsupervised part-of-speech tagger, making it truly unsupervised, requiring only unannotated text. The algorithm is discussed in detail, along with its background, and its performance. Experimentation shows that the proposed algorithm performs within 3% of the baseline, the Penn-TreeBank Lexicon.<br>Master of Science
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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 reviews as either interesting or uninteresting. We propose a workflow that would create means to categorize reviews not only in these two categories, but in multiple. The method revolved around experimentation with clustering algorithms and neural networks. Previous research shows that texts can be clustered, however, the datasets used seem to be vastly different from the Klarna dataset. The Klarna dataset consists of short reviews and contain a large amount of uninteresting reviews. Using unsupervised clustering yielded unsatisfactory results, as no discernible categories could be found. In some cases, the technique created clusters of uninteresting reviews. These clusters were used as training data for an artificial neural network, together with manually labeled interesting reviews. The results from this artificial neural network was satisfactory; it can with an accuracy of around 86% say whether a review is interesting or not. This was achieved using the aforementioned clusters and five feedback loops, where the model’s wrongfully predicted reviews from an evaluation dataset was fed back to it as training data. We argue that the main reason behind why unsupervised clustering failed is that the length of the reviews are too short. In comparison, other researchers have successfully clustered text data with an average length in the hundreds. These items pack much more features than the short reviews in the Klarna dataset. We show that an artificial neural network is able to detect these features despite the short length, through its intrinsic design. Further research in feature extraction of short text strings could provide means to cluster this kind of data. If features can be extracted, the clustering can thus be done on the features rather than the actual words. Our artificial neural network shows that the arbitrary features interesting and uninteresting can be extracted, so we are hopeful that future researchers will find ways of extracting more features from short text strings. In theory, this should mean that text of all lengths can be clustered unsupervised.<br>Databaser med användargenererad data kan snabbt bli ohanterbara. Klarna stod inför detta problem, med en databas innehållande cirka 700 000 recensioner från kunder. De såg helst att databasen skulle rensas från ointressanta recensioner och att de kvarvarande kategoriseras. Eftersom att kategorierna var okända initialt, var tanken att använda en oövervakad grupperingsalgoritm. Denna rapport beskriver det arbete som utfördes för att lösa detta problem, och föreslår en lösning till Klarna som involverar artificiella neurala nätverk istället för oövervakad gruppering. Implementationen skapad av oss är kapabel till att kategorisera recensioner som intressanta eller ointressanta. Vi föreslår ett arbetsflöde som skulle skapa möjlighet att kategorisera recensioner inte bara i dessa två kategorier, utan i flera. Metoden kretsar kring experimentering med grupperingsalgoritmer och artificiella neurala nätverk. Tidigare forskning visar att texter kan grupperas oövervakat, dock med ingångsdata som väsentligt skiljer sig från Klarnas data. Recensionerna i Klarnas data är generellt sett korta och en stor andel av dem kan ses som ointressanta. Oövervakad grupperingen gav otillräckliga resultat, då inga skönjbara kategorier stod att finna. I vissa fall skapades grupperingar av ointressanta recensioner. Dessa användes som träningsdata för ett artificiellt neuralt nätverk. Till träningsdatan lades intressanta recensioner som tagits fram manuellt. Resultaten från detta var positivt; med en träffsäkerhet om cirka 86% avgörs om en recension är intressant eller inte. Detta uppnåddes genom den tidigare skapade träningsdatan samt fem återkopplingsprocesser, där modellens felaktiga prediktioner av evalueringsdata matades in som träningsdata. Vår uppfattning är att den korta längden på recensionerna gör att den oövervakade grupperingen inte fungerar. Andra forskare har lyckats gruppera textdata med snittlängder om hundratals ord per text. Dessa texter rymmer fler meningsfulla enheter än de korta recensionerna i Klarnas data. Det finns lösningar som innefattar artificiella neurala nätverk å andra sidan kan upptäcka dessa meningsfulla enheter, tack vare sin grundläggande utformning. Vårt arbete visar att ett artificiellt neuralt nätverk kan upptäcka dessa meningsfulla enheter, trots den korta längden per recension. Extrahering av meningsfulla enheter ur korta texter är ett ¨ämne som behöver mer forskning för att underlätta problem som detta. Om meningsfulla enheter kan extraheras ur texter, kan grupperingen göras på dessa enheter istället för orden i sig. Vårt artificiella neurala nätverk visar att de arbiträra enheterna intressant och ointressant kan extraheras, vilket gör oss hoppfulla om att framtida forskare kan finna sätt att extrahera fler enheter ur korta texter. I teorin innebär detta att texter av alla längder kan grupperas oövervakat.
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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 paper, we proposed an unsupervised learning method, which automatically extracts keywords from unlabeled documents for text categorization. We regard Wikipedia as knowledge resource, and extract words which appeared with keywords for enhancing the keyword list. The experimental results show that our method is better than other term weighting methods based on frequency in text classification.
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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, because the bots may get suspended or deleted, bots should be observed in smaller groups, based on their characteristics, as types. Yet, most of the existing research on social media bot detection is focused on labeling bot accounts by only distinguishing them from human accounts and may ignore differences between individual bot accounts. The consideration of these bots' types may be the best solution for researchers and social media companies alike as it is in both of their best interests to study these types separately. However, up until this point, bot categorization has only been theorized or done manually. Thus, the goal of this research is to automate this process of grouping bots by their respective types. To accomplish this goal, the author experimentally demonstrates that it is possible to use unsupervised machine learning to categorize bots into types based on the proposed typology by creating an aggregated dataset, subsequent to determining that the accounts within are bots, and utilizing an existing typology for bots. Having the ability to differentiate between types of bots automatically will allow social media experts to analyze bot activity, from a new perspective, on a more granular level. This way, researchers can identify patterns related to a given bot type's behaviors over time and determine if certain detection methods are more viable for that type.<br>Dissertation/Thesis<br>Presentation Materials for Thesis Defense<br>Masters Thesis Computer Science 2019
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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 manually. The task is time-consuming. To overcome those disadvantages and achieve better classification accuracy without labeled data, we propose the combination of three external sources “Wikipedia”, ”WordNet” and ”Google distance” for text classification on unsupervised learning. The result of experiments shows that the combination of Wikipedia with WordNet achieves better performance than the individual methods
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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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