Relevant bibliographies by topics / Supervised categorization

Academic literature on the topic 'Supervised categorization'

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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 capability. The nonlinear combination of similar and dissimilar input features captures more extensive information about stimulus. SD networks also display more effective convergence properties than BP networks when tested with XOR problems. Finally, in a comparative study of printed-letter categorization, the SD network model performed better than the traditional BP network.
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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 into different categories and then onto these different categories the support vector Machine (SVM) model is applied for the classification task. The purpose is to improve the performance of support vector Machine by having data already in some pattern, otherwise, support vector Machine will take a lot of time in the training of data set.
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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, Naïve Bayes, Support Vector Machine (SVM), and Decision Tree, have been examined, and the algorithm with the highest results has been compared with Auranest's existing filter. Accuracy, Precision, Recall, and F1 score have been used to determine which machine learning algorithm received the highest results and in comparison, with Auranest's filter. The results showed that the supervised machine learning algorithm SVM achieved the best results in all metrics. The comparison between Auranest's existing filter and SVM showed that SVM performed better in all calculated metrics, where the accuracy showed 99.5% for SVM and 93.03% for Auranest’s filter. The comparative results showed that accuracy was the only factor that received similar results. For the other metrics, there was a noticeable difference.
Dagens samhälle blir alltmer digitaliserat och ett vanligt kommunikationssätt är att skicka e-postmeddelanden. I dagsläget har företaget Auranest ett filter för att kategorisera e-postmeddelanden men filtret är några år gammalt. Användningsområdet för filtret är att sortera ut värdefulla e-postmeddelanden för arbetssökande, där kontakt kan ske från arbetsgivare. Företaget vill veta ifall kategoriseringen kan göras med en annan metod samt förbättras. Målet med examensarbetet är att undersöka ifall filtreringen kan göras med högre träffsäkerhet med hjälp av maskininlärning. Tre övervakade maskininlärningsalgoritmer, Naïve Bayes, Support Vector Machine (SVM) och Decision Tree, har granskats och algoritmen med de högsta resultaten har jämförts med Auranests befintliga filter. Träffsäkerhet, precision, känslighet och F1-poäng har använts för att avgöra vilken maskininlärningsalgoritm som gav högst resultat sinsemellan samt i jämförelse med Auranests filter. Resultatet påvisade att den övervakade maskininlärningsmetoden SVM åstadkom de främsta resultaten i samtliga mätvärden. Jämförelsen mellan Auranests befintliga filter och SVM visade att SVM presterade bättre i alla kalkylerade mätvärden, där träffsäkerheten visade 99,5% för SVM och 93,03% för Auranests filter. De jämförande resultaten visade att träffsäkerheten var den enda faktorn som gav liknande resultat. För de övriga mätvärdena var det en märkbar skillnad.
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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 to object pose. Like many other computer vision tasks, FGVC has witnessed phenomenal advancement following the resurgence of deep neural networks. However, the proposed deep models are usually treated as black boxes. Network interpretation and understanding aims to unveil the features learned by neural networks and explain the reason behind network decisions. It is not only a necessary component for building trust between humans and algorithms, but also an essential step towards continuous improvement in this field. This dissertation is a collection of papers that contribute to FGVC and neural network interpretation and understanding. Our first contribution is an algorithm named Pose and Appearance Integration for Recognizing Subcategories (PAIRS) which performs pose estimation and generates a unified object representation as the concatenation of pose-aligned region features. As the second contribution, we propose the task of semantic network interpretation. For filter interpretation, we represent the concepts a filter detects using an attribute probability density function. We propose the task of semantic attribution using textual summarization that generates an explanatory sentence consisting of the most important visual attributes for decision-making, as found by a general Bayesian inference algorithm. Pooling has been a key component in convolutional neural networks and is of special interest in FGVC. Our third contribution is an empirical and experimental study towards a thorough yet intuitive understanding and extensive benchmark of popular pooling approaches. Our fourth contribution is a novel LMPNet for weakly-supervised keypoint discovery. A novel leaky max pooling layer is proposed to explicitly encourages sparse feature maps to be learned. A learnable clustering layer is proposed to group the keypoint proposals into final keypoint predictions. 2020 marks the 10th year since the beginning of fine-grained visual categorization. It is of great importance to summarize the representative works in this domain. Our last contribution is a comprehensive survey of FGVC containing nearly 200 relevant papers that cover 7 common themes.
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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 dimension élevée. Le travail de cette thèse consiste à développer des techniques d'apprentissage de représentation de données à base de graphes, appliquées à la reconnaissance de formes. Plus précisément, les expérimentations sont conduites sur des bases de données correspondant à plusieurs catégories d'images publiques telles que les bases de visages, les bases de scènes intérieures et extérieures, les bases d’objets, etc. Plusieurs approches sont proposées dans cette thèse : 1) Une nouvelle méthode non linéaire appelée inclusion discriminante flexible basée sur un graphe avec sélection de caractéristiques est proposée. Nous recherchons une représentation non linéaire et linéaire des données pouvant convenir à des tâches d'apprentissage génériques telles que la classification et le regroupement. En outre, un résultat secondaire de la méthode proposée est la sélection de caractéristiques originales, où la matrice de transformation linéaire estimée peut-être utilisée pour le classement et la sélection de caractéristiques. 2) Pour l'obtention d'une représentation non linéaire flexible et inductive des données, nous développons et étudions des stratégies et des algorithmes qui estiment simultanément la représentation de données désirée et une pondération explicite de caractéristiques. Le critère proposé estime explicitement les poids des caractéristiques ainsi que les données projetées et la transformation linéaire de sorte que la régularité des données et de grandes marges soient obtenues dans l'espace de projection. De plus, nous introduisons une variante à base de noyaux du modèle afin d'obtenir une représentation de données non linéaire inductive proche d'un véritable sous-espace non linéaire pour une bonne approximation des données. 3) Un apprentissage profond flexible qui peut surmonter les limites et les faiblesses des modèles d'apprentissage à une seule couche est introduit. Nous appelons cette stratégie une représentation basée sur un graphe élastique avec une architecture profonde qui explore en profondeur les informations structurelles des données. Le cadre résultant peut être utilisé pour les environnements semi-supervisés et supervisés. De plus, les problèmes d'optimisation qui en résultent peuvent être résolus efficacement. 4) Nous proposons une méthode semi-supervisée pour la représentation de données qui exploite la notion de convolution avec graphes. Cette méthode offre une nouvelle perspective de recherche sur la représentation de données non linéaires et établit un lien avec le traitement du signal sur les méthodes à base de graphes. La méthode proposée utilise et exploite les graphes de deux manières. Tout d'abord, il déploie une régularité des données sur les graphes. Deuxièmement, son modèle de régression est construit sur l'utilisation conjointe des données et de leur graphe en ce sens que le modèle de régression fonctionne avec des données convolutées. Ces dernières sont obtenues par propagation de caractéristiques
Graph-based Manifold Learning algorithms are regarded as a powerful technique for feature extraction and dimensionality reduction in Pattern Recogniton, Computer Vision and Machine Learning fields. These algorithms utilize sample information contained in the item-item similarity and weighted matrix to reveal the intrinstic geometric structure of manifold. It exhibits the low dimensional structure in the high dimensional data. This motivates me to develop Graph-based Manifold Learning techniques on Pattern Recognition, specially, application to image categorization. The experimental datasets of thesis correspond to several categories of public image datasets such as face datasets, indoor and outdoor scene datasets, objects datasets and so on. Several approaches are proposed in this thesis: 1) A novel nonlinear method called Flexible Discriminant graph-based Embedding with feature selection (FDEFS) is proposed. We seek a non-linear and a linear representation of the data that can be suitable for generic learning tasks such as classification and clustering. Besides, a byproduct of the proposed embedding framework is the feature selection of the original features, where the estimated linear transformation matrix can be used for feature ranking and selection. 2) We investigate strategies and related algorithms to develop a joint graph-based embedding and an explicit feature weighting for getting a flexible and inductive nonlinear data representation on manifolds. The proposed criterion explicitly estimates the feature weights together with the projected data and the linear transformation such that data smoothness and large margins are achieved in the projection space. Moreover, this chapter introduces a kernel variant of the model in order to get an inductive nonlinear embedding that is close to a real nonlinear subspace for a good approximation of the embedded data. 3) We propose the graph convolution based semi-supervised Embedding (GCSE). It provides a new perspective to non-linear data embedding research, and makes a link to signal processing on graph methods. The proposed method utilizes and exploits graphs in two ways. First, it deploys data smoothness over graphs. Second, its regression model is built on the joint use of the data and their graph in the sense that the regression model works with convolved data. The convolved data are obtained by feature propagation. 4) A flexible deep learning that can overcome the limitations and weaknesses of single-layer learning models is introduced. We call this strategy an Elastic graph-based embedding with deep architecture which deeply explores the structural information of the data. The resulting framework can be used for semi-supervised and supervised settings. Besides, the resulting optimization problems can be solved efficiently
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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 follows two angles: studying the behavior of (dis)similarity metrics with increasing dimensionality, and exploring feature-selection methods, primarily with regard to document representation schemes for text classification. The main results of the dissertation, relevant to the first research angle, include theoretical insights into the concentration behavior of cosine similarity, and a detailed analysis of the phenomenon of hubness, which refers to the tendency of some points in a data set to become hubs by being in-cluded in unexpectedly many k-nearest neighbor lists of other points. The mechanisms behind the phenomenon are studied in detail, both from a theoretical and empirical perspective, linking hubness with the (intrinsic) dimensionality of data, describing its interaction with the cluster structure of data and the information provided by class la-bels, and demonstrating the interplay of the phenomenon and well known algorithms for classification, semi-supervised learning, clustering, and outlier detection, with special consideration being given to time-series classification and information retrieval. Results pertaining to the second research angle include quantification of the interaction between various transformations of high-dimensional document representations, and feature selection, in the context of text classification.
U tekućem „informatičkom dobu“, masivne količine podataka sesakupljaju brzinom koja ne dozvoljava njihovo efektivno strukturiranje,analizu, i pretvaranje u korisno znanje. Ovo zasićenje informacijamase manifestuje kako kroz veliki broj objekata uključenihu skupove podataka, tako i kroz veliki broj atributa, takođe poznatkao velika dimenzionalnost. Disertacija se bavi problemima kojiproizilaze iz velike dimenzionalnosti reprezentacije podataka, čestonazivanim „prokletstvom dimenzionalnosti“, u kontekstu mašinskogučenja, data mining-a i information retrieval-a. Opisana istraživanjaprate dva pravca: izučavanje ponašanja metrika (ne)sličnosti u odnosuna rastuću dimenzionalnost, i proučavanje metoda odabira atributa,prvenstveno u interakciji sa tehnikama reprezentacije dokumenata zaklasifikaciju teksta. Centralni rezultati disertacije, relevantni za prvipravac istraživanja, uključuju teorijske uvide u fenomen koncentracijekosinusne mere sličnosti, i detaljnu analizu fenomena habovitosti kojise odnosi na tendenciju nekih tačaka u skupu podataka da postanuhabovi tako što bivaju uvrštene u neočekivano mnogo lista k najbližihsuseda ostalih tačaka. Mehanizmi koji pokreću fenomen detaljno suproučeni, kako iz teorijske tako i iz empirijske perspektive. Habovitostje povezana sa (latentnom) dimenzionalnošću podataka, opisanaje njena interakcija sa strukturom klastera u podacima i informacijamakoje pružaju oznake klasa, i demonstriran je njen efekat napoznate algoritme za klasifikaciju, semi-supervizirano učenje, klasteringi detekciju outlier-a, sa posebnim osvrtom na klasifikaciju vremenskihserija i information retrieval. Rezultati koji se odnose nadrugi pravac istraživanja uključuju kvantifikaciju interakcije izmeđurazličitih transformacija višedimenzionalnih reprezentacija dokumenatai odabira atributa, u kontekstu klasifikacije teksta.
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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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10

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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