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Journal articles on the topic 'Supervised categorization'

To see the other types of publications on this topic, follow the link: Supervised categorization.

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Nguyen, Tam T., Kuiyu Chang, and Siu Cheung Hui. "Supervised term weighting centroid-based classifiers for text categorization." Knowledge and Information Systems 35, no. 1 (September 9, 2012): 61–85. http://dx.doi.org/10.1007/s10115-012-0559-9.

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12

Basu, Tanmay, and C. A. Murthy. "A supervised term selection technique for effective text categorization." International Journal of Machine Learning and Cybernetics 7, no. 5 (September 18, 2015): 877–92. http://dx.doi.org/10.1007/s13042-015-0421-y.

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13

COLREAVY, E., and S. LEWANDOWSKY. "Strategy development and learning differences in supervised and unsupervised categorization." Memory & Cognition 36, no. 4 (June 1, 2008): 762–75. http://dx.doi.org/10.3758/mc.36.4.762.

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14

Wajeed, Mohammed Abdul. "Comparison Of Supervised and Semisupervised Fuzzy Clusters in Text Categorization." International Journal of Fuzzy Logic Systems 2, no. 1 (January 31, 2012): 41–51. http://dx.doi.org/10.5121/ijfls.2012.2105.

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15

Dornaika, F., Y. El Traboulsi, and A. Assoum. "Inductive and flexible feature extraction for semi-supervised pattern categorization." Pattern Recognition 60 (December 2016): 275–85. http://dx.doi.org/10.1016/j.patcog.2016.04.024.

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16

Zhang, Yu, Xiu-Shen Wei, Jianxin Wu, Jianfei Cai, Jiangbo Lu, Viet-Anh Nguyen, and Minh N. Do. "Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation." IEEE Transactions on Image Processing 25, no. 4 (April 2016): 1713–25. http://dx.doi.org/10.1109/tip.2016.2531289.

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17

Pothos, Emmanuel M., Darren J. Edwards, and Amotz Perlman. "Supervised versus Unsupervised Categorization: Two Sides of the Same Coin?" Quarterly Journal of Experimental Psychology 64, no. 9 (September 2011): 1692–713. http://dx.doi.org/10.1080/17470218.2011.554990.

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18

Xu, Zhe, Shaoli Huang, Ya Zhang, and Dacheng Tao. "Webly-Supervised Fine-Grained Visual Categorization via Deep Domain Adaptation." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 5 (May 1, 2018): 1100–1113. http://dx.doi.org/10.1109/tpami.2016.2637331.

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19

Man Lan, Chew Lim Tan, Jian Su, and Yue Lu. "Supervised and Traditional Term Weighting Methods for Automatic Text Categorization." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 4 (April 2009): 721–35. http://dx.doi.org/10.1109/tpami.2008.110.

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20

Narayana Swamy, M., and M. Hanumanthappa. "Indian Language Text Representation and Categorization Using Supervised Learning Algorithm." International Journal of Web Technology 002, no. 002 (December 10, 2013): 40–44. http://dx.doi.org/10.20894/ijwt.104.002.002.004.

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21

Wang, Junzheng, Nanyu Li, Zhiming Luo, Zhun Zhong, and Shaozi Li. "High-Order-Interaction for weakly supervised Fine-Grained Visual Categorization." Neurocomputing 464 (November 2021): 27–36. http://dx.doi.org/10.1016/j.neucom.2021.08.108.

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22

Livieris, Ioannis, Emmanuel Pintelas, and Panagiotis Pintelas. "Gender Recognition by Voice using an Improved Self-Labeled Algorithm." Machine Learning and Knowledge Extraction 1, no. 1 (March 5, 2019): 492–503. http://dx.doi.org/10.3390/make1010030.

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Speech recognition has various applications including human to machine interaction, sorting of telephone calls by gender categorization, video categorization with tagging and so on. Currently, machine learning is a popular trend which has been widely utilized in various fields and applications, exploiting the recent development in digital technologies and the advantage of storage capabilities from electronic media. Recently, research focuses on the combination of ensemble learning techniques with the semi-supervised learning framework aiming to build more accurate classifiers. In this paper, we focus on gender recognition by voice utilizing a new ensemble semi-supervised self-labeled algorithm. Our preliminary numerical experiments demonstrate the classification efficiency of the proposed algorithm in terms of accuracy, leading to the development of stable and robust predictive models.

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23

Xia, Rong Ze, Yan Jia, and Hu Li. "A Text Categorization Method Based on SVM and Improved K-Means." Applied Mechanics and Materials 427-429 (September 2013): 2449–53. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2449.

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Traditional supervised classification method such as support vector machine (SVM) could achieve high performance in text categorization. However, we should first hand-labeled the samples before classifying. Its a time-consuming task. Unsupervised method such as k-means could also be used for handling the text categorization problem. However, Traditional k-means could easily be affected by several isolated observations. In this paper, we proposed a new text categorization method. First we improved the traditional k-means clustering algorithm. The improved k-means is used for clustering vectors in our vector space model. After that, we use the SVM to categorize vectors which are preprocessed by improved k-means. The experiments show that our algorithm could out-perform the traditional SVM text categorization method.

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24

Zou, Huan Xin, Hao Sun, and Ke Feng Ji. "Discriminative Action Recognition Using Supervised Latent Topic Model." Applied Mechanics and Materials 190-191 (July 2012): 1125–28. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.1125.

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We present a discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure which is most relevant to action categorization. We test our algorithm on two challenging datasets. Experimental results demonstrate the effectiveness of our algorithm.

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25

Keyvanpour, Mohammad Reza, and Maryam Bahojb Imani. "Semi-supervised text categorization: Exploiting unlabeled data using ensemble learning algorithms." Intelligent Data Analysis 17, no. 3 (May 16, 2013): 367–85. http://dx.doi.org/10.3233/ida-130584.

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26

Skarmeta, Antonio G�mez, Amine Bensaid, and Nadia Tazi. "Data mining for text categorization with semi-supervised agglomerative hierarchical clustering." International Journal of Intelligent Systems 15, no. 7 (July 2000): 633–46. http://dx.doi.org/10.1002/(sici)1098-111x(200007)15:7<633::aid-int4>3.0.co;2-8.

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27

Lee, Chung-Hong, and Hsin-Chang Yang. "Construction of supervised and unsupervised learning systems for multilingual text categorization." Expert Systems with Applications 36, no. 2 (March 2009): 2400–2410. http://dx.doi.org/10.1016/j.eswa.2007.12.052.

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28

Patel, Chandrakant D., and Jayesh M. Patel. "Influence of GUJarati STEmmeR in Supervised Learning of Web Page Categorization." International Journal of Intelligent Systems and Applications 13, no. 3 (June 8, 2021): 23–34. http://dx.doi.org/10.5815/ijisa.2021.03.03.

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With the large quantity of information offered on-line, it's equally essential to retrieve correct information for a user query. A large amount of data is available in digital form in multiple languages. The various approaches want to increase the effectiveness of on-line information retrieval but the standard approach tries to retrieve information for a user query is to go looking at the documents within the corpus as a word by word for the given query. This approach is incredibly time intensive and it's going to miss several connected documents that are equally important. So, to avoid these issues, stemming has been extensively utilized in numerous Information Retrieval Systems (IRS) to extend the retrieval accuracy of all languages. These papers go through the problem of stemming with Web Page Categorization on Gujarati language which basically derived the stem words using GUJSTER algorithms [1]. The GUJSTER algorithm is based on morphological rules which is used to derived root or stem word from inflected words of the same class. In particular, we consider the influence of extracted a stem or root word, to check the integrity of the web page classification using supervised machine learning algorithms. This research work is intended to focus on the analysis of Web Page Categorization (WPC) of Gujarati language and concentrate on a research problem to do verify the influence of a stemming algorithm in a WPC application for the Gujarati language with improved accuracy between from 63% to 98% through Machine Learning supervised models with standard ratio 80% as training and 20% as testing.

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29

Caceres, Noelia, and Francisco G. Benitez. "Supervised Land Use Inference from Mobility Patterns." Journal of Advanced Transportation 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/8710402.

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This paper addresses the relationship between land use and mobility patterns. Since each particular zone directly feeds the global mobility once acting as origin of trips and others as destination, both roles are simultaneously used for predicting land uses. Specifically this investigation uses mobility data derived from mobile phones, a technology that emerges as a useful, quick data source on people’s daily mobility, collected during two weeks over the urban area of Malaga (Spain). This allows exploring the relevance of integrating weekday-weekend trip information to better determine the category of land use. First, this work classifies patterns on trips originated and terminated in each zone into groups by means of a clustering approach. Based on identifiable relationships between activity and times when travel peaks appear, a preliminary categorization of uses is provided. Then, both grouping results are used as input variables in a K-nearest neighbors (KNN) classification model to determine the exact land use. The KNN method assumes that the category of an object must be similar to the category of the closest neighbors. After training the models, the findings reveal that this approach provides a precise land use categorization, yielding the best accuracy results for the major categories of land uses in the studied area. Moreover, as a result, the weekend data certainly contributes to finding more precise land uses as those obtained by just weekday data. In particular, the percentage of correctly predicted categories using both weekday and weekend is around 80%, while just weekday data reach 67%. The comparison with actual land uses also demonstrates that this approach is able to provide useful information, identifying zones with a specific clear dominant use (residential, industrial, and commercial), as well as multiactivity zones (mixed). This fact is especially useful in the context of urban environments where multiple activities coexist.

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30

Iwata, Tomoharu, Kazumi Saito, Naonori Ueda, Sean Stromsten, Thomas L. Griffiths, and Joshua B. Tenenbaum. "Parametric Embedding for Class Visualization." Neural Computation 19, no. 9 (September 2007): 2536–56. http://dx.doi.org/10.1162/neco.2007.19.9.2536.

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We propose a new method, parametric embedding (PE), that embeds objects with the class structure into a low-dimensional visualization space. PE takes as input a set of class conditional probabilities for given data points and tries to preserve the structure in an embedding space by minimizing a sum of Kullback-Leibler divergences, under the assumption that samples are generated by a gaussian mixture with equal covariances in the embedding space. PE has many potential uses depending on the source of the input data, providing insight into the classifier's behavior in supervised, semisupervised, and unsupervised settings. The PE algorithm has a computational advantage over conventional embedding methods based on pairwise object relations since its complexity scales with the product of the number of objects and the number of classes. We demonstrate PE by visualizing supervised categorization of Web pages, semisupervised categorization of digits, and the relations of words and latent topics found by an unsupervised algorithm, latent Dirichlet allocation.

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31

Basu, Tanmay, and C. A. Murthy. "A Similarity Based Supervised Decision Rule for Qualitative Improvement of Text Categorization." Fundamenta Informaticae 141, no. 4 (December 9, 2015): 275–95. http://dx.doi.org/10.3233/fi-2015-1276.

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32

Zhang, Yabin, Kui Jia, and Zhixin Wang. "Part-Aware Fine-Grained Object Categorization Using Weakly Supervised Part Detection Network." IEEE Transactions on Multimedia 22, no. 5 (May 2020): 1345–57. http://dx.doi.org/10.1109/tmm.2019.2939747.

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33

Cong, Yang, Ji Liu, Junsong Yuan, and Jiebo Luo. "Self-Supervised Online Metric Learning With Low Rank Constraint for Scene Categorization." IEEE Transactions on Image Processing 22, no. 8 (August 2013): 3179–91. http://dx.doi.org/10.1109/tip.2013.2260168.

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34

Ciocca, Gianluigi, Claudio Cusano, Simone Santini, and Raimondo Schettini. "On the use of supervised features for unsupervised image categorization: An evaluation." Computer Vision and Image Understanding 122 (May 2014): 155–71. http://dx.doi.org/10.1016/j.cviu.2014.01.010.

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35

Limsettho, Nachai, Hideaki Hata, Akito Monden, and Kenichi Matsumoto. "Unsupervised Bug Report Categorization Using Clustering and Labeling Algorithm." International Journal of Software Engineering and Knowledge Engineering 26, no. 07 (September 2016): 1027–53. http://dx.doi.org/10.1142/s0218194016500352.

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Bug reports are one of the most crucial information sources for software engineering offering answers to many questions. Yet, getting these answers is not always easy; the information in bug reports is often implicit and some processes are required to extract the meaning of these reports. Most research in this area employ a supervised learning approach to classify bug reports so that required types of reports could be identified. However, this approach often requires an immense amount of time and effort, the resources that already too scarce in many projects. We aim to develop an automated framework that can categorize bug reports, according to their grammatical structure without the need for labeled data. Our framework categorizes bug reports according to their text similarity using topic modeling and a clustering algorithm. Each group of bug reports are labeled with our new clustering labeling algorithm specifically made for clusters in the topic space. Our framework is highly customizable with a modular approach and options to incorporate available background knowledge to improve its performance, while our cluster labeling approach make use of natural language process (NLP) chunking to create the representative labels. Our experiment results demonstrate that the performance of our unsupervised framework is comparable to a supervised learning one. We also show that our labeling process is capable of labeling each cluster with phrases that are representative for that cluster's characteristics. Our framework can be used to automatically categorize the incoming bug reports without any prior knowledge, as an automated labeling suggestion system or as a tool for obtaining knowledge about the structure of the bug report repository.

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36

Abuthawabeh, Mohammad, and Khaled Mahmoud. "Enhanced Android Malware Detection and Family Classification, using Conversation-level Network Traffic Features." International Arab Journal of Information Technology 17, no. 4A (July 31, 2020): 607–14. http://dx.doi.org/10.34028/iajit/17/4a/4.

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Signature-based malware detection algorithms are facing challenges to cope with the massive number of threats in the Android environment. In this paper, conversation-level network traffic features are extracted and used in a supervised-based model. This model was used to enhance the process of Android malware detection, categorization, and family classification. The model employs the ensemble learning technique in order to select the most useful features among the extracted features. A real-world dataset called CICAndMal2017 was used in this paper. The results show that Extra-trees classifier had achieved the highest weighted accuracy percentage among the other classifiers by 87.75%, 79.97%, and 66.71%for malware detection, malware categorization, and malware family classification respectively. A comparison with another study that uses the same dataset was made. This study has achieved a significant enhancement in malware family classification and malware categorization. For malware family classification, the enhancement was 39.71% for precision and 41.09% for recall. The rate of enhancement for the Android malware categorization was 30.2% and 31.14‬% for precision and recall, respectively

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37

Yang, Guofeng, Guipeng Chen, Yong He, Zhiyan Yan, Yang Guo, and Jian Ding. "Self-Supervised Collaborative Multi-Network for Fine-Grained Visual Categorization of Tomato Diseases." IEEE Access 8 (2020): 211912–23. http://dx.doi.org/10.1109/access.2020.3039345.

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38

Traboulsi, Y. El, and F. Dornaika. "Flexible semi-supervised embedding based on adaptive loss regression: Application to image categorization." Information Sciences 444 (May 2018): 1–19. http://dx.doi.org/10.1016/j.ins.2018.02.044.

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39

He, Zhiyang, Ji Wu, and Tao Li. "Label Correlation Mixture Model: A Supervised Generative Approach to Multilabel Spoken Document Categorization." IEEE Transactions on Emerging Topics in Computing 3, no. 2 (June 2015): 235–45. http://dx.doi.org/10.1109/tetc.2014.2377559.

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40

Dornaika, F., A. Bosaghzadeh, H. Salmane, and Y. Ruichek. "Graph-based semi-supervised learning with Local Binary Patterns for holistic object categorization." Expert Systems with Applications 41, no. 17 (December 2014): 7744–53. http://dx.doi.org/10.1016/j.eswa.2014.06.025.

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41

Soto, P. J., J. D. Bermudez, P. N. Happ, and R. Q. Feitosa. "A COMPARATIVE ANALYSIS OF UNSUPERVISED AND SEMI-SUPERVISED REPRESENTATION LEARNING FOR REMOTE SENSING IMAGE CATEGORIZATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 167–73. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-167-2019.

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<p><strong>Abstract.</strong> This work aims at investigating unsupervised and semi-supervised representation learning methods based on generative adversarial networks for remote sensing scene classification. The work introduces a novel approach, which consists in a semi-supervised extension of a prior unsupervised method, known as MARTA-GAN. The proposed approach was compared experimentally with two baselines upon two public datasets, <i>UC-MERCED</i> and <i>NWPU-RESISC45</i>. The experiments assessed the performance of each approach under different amounts of labeled data. The impact of fine-tuning was also investigated. The proposed method delivered in our analysis the best overall accuracy under scarce labeled samples, both in terms of absolute value and in terms of variability across multiple runs.</p>

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42

Perruchet, Pierre, and Annie Vinter. "Feature creation as a byproduct of attentional processing." Behavioral and Brain Sciences 21, no. 1 (February 1998): 33–34. http://dx.doi.org/10.1017/s0140525x9844010x.

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Attributing the creation of new features to categorization requirements implies that the exemplars displayed are correctly assigned to their category. This constraint limits the scope of Schyns et al.'s proposal to supervised learning. We present data suggesting that this constraint is unwarranted and we argue that feature creation is better thought of as a byproduct of the attentional, on-line processing of incoming information.

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43

Jiang, Zigui, Rongheng Lin, and Fangchun Yang. "A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data." Energies 11, no. 9 (August 26, 2018): 2235. http://dx.doi.org/10.3390/en11092235.

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Time-series smart meter data can record precisely electricity consumption behaviors of every consumer in the smart grid system. A better understanding of consumption behaviors and an effective consumer categorization based on the similarity of these behaviors can be helpful for flexible demand management and effective energy control. In this paper, we propose a hybrid machine learning model including both unsupervised clustering and supervised classification for categorizing consumers based on the similarity of their typical electricity consumption behaviors. Unsupervised clustering algorithm is used to extract the typical electricity consumption behaviors and perform fuzzy consumer categorization, followed by a proposed novel algorithm to identify distinct consumer categories and their consumption characteristics. Supervised classification algorithm is used to classify new consumers and evaluate the validity of the identified categories. The proposed model is applied to a real dataset of U.S. non-residential consumers collected by smart meters over one year. The results indicate that large or special institutions usually have their distinct consumption characteristics while others such as some medium and small institutions or similar building types may have the same characteristics. Moreover, the comparison results with other methods show the improved performance of the proposed model in terms of category identification and classifying accuracy.

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44

Feng, Songhe, Weihua Xiong, Bing Li, Congyan Lang, and Xiankai Huang. "Hierarchical sparse representation based Multi-Instance Semi-Supervised Learning with application to image categorization." Signal Processing 94 (January 2014): 595–607. http://dx.doi.org/10.1016/j.sigpro.2013.07.028.

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45

Lin, Zhongqi, Jingdun Jia, Wanlin Gao, and Feng Huang. "A novel quadruple generative adversarial network for semi-supervised categorization of low-resolution images." Neurocomputing 415 (November 2020): 266–85. http://dx.doi.org/10.1016/j.neucom.2020.05.050.

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46

Love, Bradley C. "Environment and Goals Jointly Direct Category Acquisition." Current Directions in Psychological Science 14, no. 4 (August 2005): 195–99. http://dx.doi.org/10.1111/j.0963-7214.2005.00363.x.

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Developing categorization schemes involves discovering structures in the world that support a learner's goals. Existing models of category learning, such as exemplar and prototype models, neglect the role of goals in shaping conceptual organization. Here, a clustering approach is discussed that reflects the joint influences of the environment and goals in directing category acquisition. Clusters are a flexible representational medium that exhibits properties of exemplar, prototype, and rule-based models. Clusters reflect the natural bundles of correlated features present in our environment. The clustering model Supervised and Unsupervised Stratified Incremental Adaptive Network (SUSTAIN) operates by assuming the world has a simple structure and adding complexity (i.e., clusters) when existing clusters fail to satisfy the learner's goals and thus elicit surprise. Although simple, this operation is sufficient to address findings from numerous laboratory and cross-cultural categorization studies.

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47

Aphinyanaphongs, Yindalon, Lawrence D. Fu, Zhiguo Li, Eric R. Peskin, Efstratios Efstathiadis, Constantin F. Aliferis, and Alexander Statnikov. "A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization." Journal of the Association for Information Science and Technology 65, no. 10 (March 1, 2014): 1964–87. http://dx.doi.org/10.1002/asi.23110.

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48

Chen, Peng, Peng Li, Qing Li, and Dezheng Zhang. "Semi-Supervised Fine-Grained Image Categorization Using Transfer Learning With Hierarchical Multi-Scale Adversarial Networks." IEEE Access 7 (2019): 118650–68. http://dx.doi.org/10.1109/access.2019.2934476.

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49

Geraldeli Rossi, Rafael, Alneu de Andrade Lopes, and Solange Oliveira Rezende. "Using bipartite heterogeneous networks to speed up inductive semi-supervised learning and improve automatic text categorization." Knowledge-Based Systems 132 (September 2017): 94–118. http://dx.doi.org/10.1016/j.knosys.2017.06.016.

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