Relevant bibliographies by topics / Supervised categorization

Contents

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

Academic literature on the topic 'Supervised categorization'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Supervised categorization"

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Rodrigues, Valter, and Josef Skrzypek. "COMBINING SIMILARITIES AND DISSIMILARITIES IN SUPERVISED LEARNING." International Journal of Neural Systems 02, no. 03 (January 1991): 263–73. http://dx.doi.org/10.1142/s0129065791000236.

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Categorization, as an active phase of the visual perception, must include a stage where a currently viewed exemplar of an object is compared to the previously acquired category representatives; comparisons between exemplars as opposed to simply examining one exemplar in isolation lead to improved supervised learning. An abstract model of a neuron (SD neuron) is introduced, that can compare inputs by detecting (S)imilarities and (D)issimilarities in sequentially presented stimuli. Using SD neurons in a traditional Back Error Propagation (BP) neural networks improves categorization and learning
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Arya, Vaishali, and Rashmi Agrawal. "Improvement in Text Categorization Using Semi-Supervised Approach and Lexical Chains." Journal of Computational and Theoretical Nanoscience 16, no. 12 (December 1, 2019): 5122–26. http://dx.doi.org/10.1166/jctn.2019.8573.

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Text categorization is used for assigning the class labels to the available data set or providing a conceptual view to a data set. The text categorization can be performed in two ways supervised way, and in an unsupervised way. But alone neither can perform well in the categorization of data set. So a semi-supervised model with the combination of lexical chains is used to perform the task of categorization. In the proposed semi-supervised model the lexical chains are used to determine the numbers of clusters has to be formed using k-means clustering. This ‘k-means’ will divide the data set int
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Mandal, Ashis Kumar, and Rikta Sen. "Supervised Learning Methods for Bangla Web Document Categorization." International Journal of Artificial Intelligence & Applications 5, no. 5 (September 30, 2014): 93–105. http://dx.doi.org/10.5121/ijaia.2014.5508.

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Mahmood, Amjad, Tianrui Li, Yan Yang, Hongjun Wang, and Mehtab Afzal. "Semi-supervised evolutionary ensembles for Web video categorization." Knowledge-Based Systems 76 (March 2015): 53–66. http://dx.doi.org/10.1016/j.knosys.2014.11.030.

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Li, Ximing, Jihong Ouyang, Xiaotang Zhou, You Lu, and Yanhui Liu. "Supervised labeled latent Dirichlet allocation for document categorization." Applied Intelligence 42, no. 3 (November 25, 2014): 581–93. http://dx.doi.org/10.1007/s10489-014-0595-0.

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Xu, Zewen, Jianqiang Li, Bo Liu, Jing Bi, Rong Li, and Rui Mao. "Semi-supervised learning in large scale text categorization." Journal of Shanghai Jiaotong University (Science) 22, no. 3 (May 30, 2017): 291–302. http://dx.doi.org/10.1007/s12204-017-1835-3.

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Lu, Wei, and Min-Yen Kan. "Supervised categorization of JavaScriptTM using program analysis features." Information Processing & Management 43, no. 2 (March 2007): 431–44. http://dx.doi.org/10.1016/j.ipm.2006.07.019.

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CAI, Yue-hong, Qian ZHU, Ping SUN, and Xian-yi CHENG. "Semi-supervised short text categorization based on attribute selection." Journal of Computer Applications 30, no. 4 (April 30, 2010): 1015–18. http://dx.doi.org/10.3724/sp.j.1087.2010.01015.

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ROBINS, ANTHONY V. "Incorporating Supervised Learning in the Domains Account of Categorization." Connection Science 4, no. 1 (January 1992): 45–56. http://dx.doi.org/10.1080/09540099208946603.

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Yan, Yang, Lihui Chen, and William-Chandra Tjhi. "Semi-supervised fuzzy co-clustering algorithm for document categorization." Knowledge and Information Systems 34, no. 1 (November 15, 2011): 55–74. http://dx.doi.org/10.1007/s10115-011-0454-9.

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Dissertations / Theses on the topic "Supervised categorization"

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Mathew, Thomas A. "Supervised categorization of habitual versus episodic sentences." Connect to Electronic Thesis (CONTENTdm), 2009. http://worldcat.org/oclc/456291172/viewonline.

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Mann, Anna, and Olivia Höft. "Categorization of Swedish e-mails using Supervised Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296558.

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Society today is becoming more digitalized, and a common way of communication is to send e-mails. Currently, the company Auranest has a filtering method for categorizing e-mails, but the method is a few years old. The filter provides a classification of valuable e-mails for jobseekers, where employers can make contact. The company wants to know if the categorization can be performed with a different method and improved. The degree project aims to investigate whether the categorization can be proceeded with higher accuracy using machine learning. Three supervised machine learning algorithms, Na
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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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Benbrahim, Houda. "A fuzzy semi-supervised support vector machine approach to hypertext categorization." Thesis, University of Portsmouth, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494145.

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As the web expands exponentially, the need to put some order to its content becomes apparent. Hypertext categorization, that is the automatic classification of web documents into predefined classes, came to elevate humans from that task. The extra information available in a hypertext document poses new challenges for automatic categorization. HTML tags and linked neighbourhood all provide rich information for hypertext categorization that is no available in traditional text classification.
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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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Guo, Pei. "Interpretable Fine-Grained Visual Categorization." BYU ScholarsArchive, 2021. https://scholarsarchive.byu.edu/etd/9119.

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Not all categories are created equal in object recognition. Fine-grained visual categorization (FGVC) is a branch of visual object recognition that aims to distinguish subordinate categories within a basic-level category. Examples include classifying an image of a bird into specific species like "Western Gull" or "California Gull". Such subordinate categories exhibit characteristics like small inter-category variation and large intra-class variation, making distinguishing them extremely difficult. To address such challenges, an algorithm should be able to focus on object parts and be invariant
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Zhu, Ruifeng. "Contribution to graph-based manifold learning with application to image categorization." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA015.

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Les algorithmes d'apprentissage de représentation de données à base de graphes sont considérés comme une technique puissante pour l'extraction de caractéristiques et la réduction de dimensionnalité dans les domaines de la reconnaissance de formes, la vision par ordinateur et l'apprentissage automatique. Ces algorithmes utilisent les informations contenues dans les similitudes d’échantillons (par paire) et la matrice du graphe pondéré pour révéler la structure géométrique intrinsèque de données. Ces algorithmes sont capables de récupérer une structure de faible dimension à partir de données de
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Miloš, Radovanović. "High-Dimensional Data Representations and Metrics for Machine Learning and Data Mining." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2011. https://www.cris.uns.ac.rs/record.jsf?recordId=77530&source=NDLTD&language=en.

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In the current information age, massive amounts of data are gathered, at a rate prohibiting their effective structuring, analysis, and conversion into useful knowledge. This information overload is manifested both in large numbers of data objects recorded in data sets, and large numbers of attributes, also known as high dimensionality. This dis-sertation deals with problems originating from high dimensionality of data representation, referred to as the “curse of dimensionality,” in the context of machine learning, data mining, and information retrieval. The described research follo
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Book chapters on the topic "Supervised categorization"

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Chen, Xingyu, and Mizuho Iwaihara. "Weakly-Supervised Neural Categorization of Wikipedia Articles." In Digital Libraries at the Crossroads of Digital Information for the Future, 16–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34058-2_2.

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Niaz, Usman Farrokh, and Bernard Merialdo. "Entropy Based Supervised Merging for Visual Categorization." In Advanced Concepts for Intelligent Vision Systems, 420–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33140-4_37.

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Debole, Franca, and Fabrizio Sebastiani. "Supervised Term Weighting for Automated Text Categorization." In Text Mining and its Applications, 81–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45219-5_7.

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Türker, Rima, Lei Zhang, Mehwish Alam, and Harald Sack. "Weakly Supervised Short Text Categorization Using World Knowledge." In Lecture Notes in Computer Science, 584–600. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62419-4_33.

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Zhu, Yan, Liping Jing, and Jian Yu. "New Labeling Strategy for Semi-supervised Document Categorization." In Knowledge Science, Engineering and Management, 134–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10488-6_16.

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Mahmood, Amjad, Tianrui Li, Yan Yang, Hongjun Wang, and Mehtab Afzal. "Semi-supervised Clustering Ensemble for Web Video Categorization." In Multiple Classifier Systems, 190–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38067-9_17.

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Lu, Wei, and Min-Yen Kan. "Supervised Categorization of JavaScriptTM Using Program Analysis Features." In Information Retrieval Technology, 160–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_13.

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Gowda, Harsha S., Mahamad Suhil, D. S. Guru, and Lavanya Narayana Raju. "Semi-supervised Text Categorization Using Recursive K-means Clustering." In Communications in Computer and Information Science, 217–27. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4859-3_20.

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Wei, Bifan, Boqin Feng, Feijuan He, and Xiaoyu Fu. "An Extended Supervised Term Weighting Method for Text Categorization." In Proceedings of the International Conference on Human-centric Computing 2011 and Embedded and Multimedia Computing 2011, 87–99. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2105-0_11.

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Li, Shoushan, Sophia Yat Mei Lee, Wei Gao, and Chu-Ren Huang. "Semi-supervised Text Categorization by Considering Sufficiency and Diversity." In Communications in Computer and Information Science, 105–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41644-6_11.

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Conference papers on the topic "Supervised categorization"

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Aldershoff, Frank, Alfons H. Salden, Sorin M. Iacob, and Masja Kempen. "Supervised multimedia categorization." In Electronic Imaging 2003, edited by Minerva M. Yeung, Rainer W. Lienhart, and Chung-Sheng Li. SPIE, 2003. http://dx.doi.org/10.1117/12.476242.

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Thung, Ferdian, Xuan-Bach D. Le, and David Lo. "Active Semi-supervised Defect Categorization." In 2015 IEEE 23rd International Conference on Program Comprehension (ICPC). IEEE, 2015. http://dx.doi.org/10.1109/icpc.2015.15.

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Hu, Jianying, Moninder Singh, and Aleksandra Mojsilovic. "Categorization using semi-supervised clustering." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761253.

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Yu, Zhenghua. "Image Categorization with Semi-Supervised Learning." In 2006 International Conference on Image Processing. IEEE, 2006. http://dx.doi.org/10.1109/icip.2006.313043.

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Pan, Xin, and Suli Zhang. "Semi-supervised fuzzy learning in text categorization." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019630.

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Quadery, Fahim, Abdullah Al Maruf, Tamjid Ahmed, and Md Saiful Islam. "Semi supervised keyword based bengali document categorization." In 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT). IEEE, 2016. http://dx.doi.org/10.1109/ceeict.2016.7873040.

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Benmokhtar, Rachid, Jonathan Delhumeau, and Philippe-Henri Gosselin. "Efficient supervised dimensionality reduction for image categorization." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638090.

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Debole, Franca, and Fabrizio Sebastiani. "Supervised term weighting for automated text categorization." In the 2003 ACM symposium. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/952532.952688.

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Xu, Zenglin, Rong Jin, Kaizhu Huang, Michael R. Lyu, and Irwin King. "Semi-supervised text categorization by active search." In Proceeding of the 17th ACM conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1458082.1458364.

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Zhang, Yu, Yu Meng, Jiaxin Huang, Frank F. Xu, Xuan Wang, and Jiawei Han. "Minimally Supervised Categorization of Text with Metadata." In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397271.3401168.

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