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Journal articles on the topic 'Unsupervised categorization'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

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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2

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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4

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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6

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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9

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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Begin, J., and R. Proulx. "Categorization in unsupervised neural networks: the Eidos model." IEEE Transactions on Neural Networks 7, no. 1 (January 1996): 147–54. http://dx.doi.org/10.1109/72.478399.

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Ashby, F. Gregory, Sarah Queller, and Patricia M. Berretty. "On the dominance of unidimensional rules in unsupervised categorization." Perception & Psychophysics 61, no. 6 (August 1999): 1178–99. http://dx.doi.org/10.3758/bf03207622.

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13

Yan, Xiaoqiang, Yangdong Ye, and Zhengzheng Lou. "Unsupervised video categorization based on multivariate information bottleneck method." Knowledge-Based Systems 84 (August 2015): 34–45. http://dx.doi.org/10.1016/j.knosys.2015.03.028.

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14

Yan, Xiaoqiang, Yangdong Ye, Xueying Qiu, Milos Manic, and Hui Yu. "CMIB: Unsupervised Image Object Categorization in Multiple Visual Contexts." IEEE Transactions on Industrial Informatics 16, no. 6 (June 2020): 3974–86. http://dx.doi.org/10.1109/tii.2019.2939278.

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15

Cui, Peng, Fei Wang, Li-Feng Sun, Jian-Wei Zhang, and Shi-Qiang Yang. "A Matrix-Based Approach to Unsupervised Human Action Categorization." IEEE Transactions on Multimedia 14, no. 1 (February 2012): 102–10. http://dx.doi.org/10.1109/tmm.2011.2176110.

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16

Edwards, Darren J. "Unsupervised categorization with a child sample: category cohesion development." European Journal of Developmental Psychology 14, no. 1 (March 23, 2016): 75–86. http://dx.doi.org/10.1080/17405629.2016.1158706.

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Hossain, Md Shafayat, Ahmedullah Aziz, and Mohammad Wahidur Rahman. "Unsupervised Object Matching and Categorization via Agglomerative Correspondence Clustering." Signal & Image Processing : An International Journal 4, no. 1 (February 28, 2013): 35–47. http://dx.doi.org/10.5121/sipij.2013.4103.

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18

Wang, Peng, Zhi-Qiang Liu, and Shi-Qiang Yang. "Investigation on unsupervised clustering algorithms for video shot categorization." Soft Computing 11, no. 4 (August 8, 2006): 355–60. http://dx.doi.org/10.1007/s00500-006-0089-z.

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19

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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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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21

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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22

Wang, Shiping, Jinyu Cai, Qihao Lin, and Wenzhong Guo. "An Overview of Unsupervised Deep Feature Representation for Text Categorization." IEEE Transactions on Computational Social Systems 6, no. 3 (June 2019): 504–17. http://dx.doi.org/10.1109/tcss.2019.2910599.

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23

JUPP, JULIE, and JOHN S. GERO. "Visual style: Qualitative and context-dependent categorization." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 20, no. 3 (June 27, 2006): 247–66. http://dx.doi.org/10.1017/s0890060406060197.

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Style is an ordering principle by which to structure artifacts in a design domain. The application of a visual order entails some explicit grouping property that is both cognitively plausible and contextually dependent. Central to cognitive–contextual notions are the type of representation used in analysis and the flexibility to allow semantic interpretation. We present a model of visual style based on the concept of similarity as a qualitative context-dependent categorization. The two core components of the model are semantic feature extraction and self-organizing maps (SOMs). The model proposes a method of categorizing two-dimensional unannotated design diagrams using both low-level geometric and high-level semantic features that are automatically derived from the pictorial content of the design. The operation of the initial model, called Q-SOM, is then extended to include relevance feedback (Q-SOM:RF). The extended model can be seen as a series of sequential processing stages, in which qualitative encoding and feature extraction are followed by iterative recategorization. Categorization is achieved using an unsupervised SOM, and contextual dependencies are integrated via cluster relevance determined by the observer's feedback. The following stages are presented: initial per feature detection and extraction, selection of feature sets corresponding to different spatial ontologies, unsupervised categorization of design diagrams based on appropriate feature subsets, and integration of design context via relevance feedback. From our experiments we compare different outcomes from consecutive stages of the model. The results show that the model provides a cognitively plausible and context-dependent method for characterizing visual style in design.

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24

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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25

Edwards, Darren J., Amotz Perlman, and Phil Reed. "Unsupervised Categorization in a sample of children with autism spectrum disorders." Research in Developmental Disabilities 33, no. 4 (July 2012): 1264–69. http://dx.doi.org/10.1016/j.ridd.2012.02.021.

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26

Xu, Tao, and Qinke Peng. "Extended information inference model for unsupervised categorization of web short texts." Journal of Information Science 38, no. 6 (October 15, 2012): 512–31. http://dx.doi.org/10.1177/0165551512448985.

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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

Lozano, M. A., F. Escolano, B. Bonev, P. Suau, W. Aguilar, J. M. Saez, and M. A. Cazorla. "Region and constellations based categorization of images with unsupervised graph learning." Image and Vision Computing 27, no. 7 (June 2009): 960–78. http://dx.doi.org/10.1016/j.imavis.2008.09.011.

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29

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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30

Löw, Andreas, Shlomo Bentin, Brigitte Rockstroh, Yaron Silberman, Annette Gomolla, Rudolf Cohen, and Thomas Elbert. "Semantic Categorization in the Human Brain." Psychological Science 14, no. 4 (July 2003): 367–72. http://dx.doi.org/10.1111/1467-9280.24451.

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We examined the cortical representation of semantic categorization using magnetic source imaging in a task that revealed both dissociations among superordinate categories and associations among different base-level concepts within these categories. Around 200 ms after stimulus onset, the spatiotemporal correlation of brain activity elicited by base-level concepts was greater within than across superordinate categories in the right temporal lobe. Unsupervised clustering of data showed similar categorization between 210 and 450 ms mainly in the left hemisphere. This pattern suggests that well-defined semantic categories are represented in spatially distinct, macroscopically separable neural networks, independent of physical stimulus properties. In contrast, a broader, task-required categorization (natural/man-made) was not evident in our data. The perceptual dynamics of the categorization process is initially evident in the extrastriate areas of the right hemisphere; this activation is followed by higher-level activity along the ventral processing stream, implicating primarily the left temporal lobe.

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31

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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32

Robinson, Saul. "Multi-Label Classification of Contributing Causal Factors in Self-Reported Safety Narratives." Safety 4, no. 3 (July 20, 2018): 30. http://dx.doi.org/10.3390/safety4030030.

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Three methods are demonstrated for automated classification of aviation safety narratives within an existing complex taxonomy. Utilizing latent semantic analysis trained against 4497 narratives at the sentence level, primary problem and contributing factor labels were assessed. Results from a sample of 2987 narratives provided a mean unsupervised categorization precision of 0.35% and recall of 0.78% for contributing-factors within the taxonomy. Categorization of the primary problem at the sentence level resulted in a modal accuracy of 0.46%. Overall, the results suggested that the demonstrated approaches were viable in bringing additional tools and insights to safety researchers.

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33

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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34

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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35

Yan, Xiaoqiang, Zhengzheng Lou, Shizhe Hu, and Yangdong Ye. "Multi-task Information Bottleneck Co-clustering for Unsupervised Cross-view Human Action Categorization." ACM Transactions on Knowledge Discovery from Data 14, no. 2 (March 7, 2020): 1–23. http://dx.doi.org/10.1145/3375394.

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36

Song, Wei, Lim Cheon Choi, Soon Cheol Park, and Xiao Feng Ding. "Fuzzy evolutionary optimization modeling and its applications to unsupervised categorization and extractive summarization." Expert Systems with Applications 38, no. 8 (August 2011): 9112–21. http://dx.doi.org/10.1016/j.eswa.2010.12.102.

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37

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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38

Guo, Lin, Wanli Zuo, Tao Peng, and Lin Yue. "Text Matching and Categorization: Mining Implicit Semantic Knowledge from Tree-Shape Structures." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/723469.

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The diversities of large-scale semistructured data make the extraction of implicit semantic information have enormous difficulties. This paper proposes an automatic and unsupervised method of text categorization, in which tree-shape structures are used to represent semantic knowledge and to explore implicit information by mining hidden structures without cumbersome lexical analysis. Mining implicit frequent structures in trees can discover both direct and indirect semantic relations, which largely enhances the accuracy of matching and classifying texts. The experimental results show that the proposed algorithm remarkably reduces the time and effort spent in training and classifying, which outperforms established competitors in correctness and effectiveness.

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Edwards, Darren J., and Rodger Wood. "Unsupervised categorization with individuals diagnosed as having moderate traumatic brain injury: Over-selective responding." Brain Injury 30, no. 13-14 (September 14, 2016): 1576–80. http://dx.doi.org/10.1080/02699052.2016.1199899.

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40

Dong, Yuan, Nan Zhao, Shiguo Lian, Shusheng Cen, and Wei Liu. "Unsupervised mining of visually consistent shots for sports genre categorization over large-scale database." Telecommunication Systems 59, no. 3 (December 12, 2014): 381–91. http://dx.doi.org/10.1007/s11235-014-9943-y.

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41

Reza, Nosheen, William Bone, Pankhuri Singhal, Anurag Verma, Ashwin C. Murthy, Srinivas Denduluri, Srinath Adusumalli, Macrylyn D. Ritchie, and Thomas P. Cappola. "42855 A Phenomics Approach to the Categorization and Refinement of Heart Failure." Journal of Clinical and Translational Science 5, s1 (March 2021): 46–47. http://dx.doi.org/10.1017/cts.2021.524.

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ABSTRACT IMPACT: Measuring and analyzing qualitative and quantitative traits using phenomics approaches will yield previously unrecognized heart failure subphenotypes and has the potential to improve our knowledge of heart failure pathophysiology, identify novel biomarkers of disease, and guide the development of targeted therapeutics for heart failure. OBJECTIVES/GOALS: Current classification schemes fail to capture the broader pathophysiologic heterogeneity in heart failure. Phenomics offers a newer unbiased approach to identify subtypes of complex disease syndromes, like heart failure. The goal of this research is to use data-driven associations to redefine the classification of the heart failure syndrome. METHODS/STUDY POPULATION: We will identify < 10 subphenotypes of patients with heart failure using unsupervised machine learning approaches for dense multidimensional quantitative (i.e. demographics, comorbid conditions, physiologic measurements, clinical laboratory, imaging, and medication variables; disease diagnosis, procedure, and billing codes) and qualitative data extracted from an integrated health system electronic health record. The heart failure subphenotypes we identify from the integrated health system electronic health record will be replicated in other heart failure population datasets using unsupervised learning approaches. We will explore the potential to establish associations between identified subphenotypes and clinical outcomes (e.g. all-cause mortality, cardiovascular mortality). RESULTS/ANTICIPATED RESULTS: We expect to identify < 10 mutually exclusive phenogroups of patients with heart failure that have differential risk profiles and clinical trajectories. DISCUSSION/SIGNIFICANCE OF FINDINGS: We will attempt to derive and validate a data-driven unbiased approach to the categorization of novel phenogroups in heart failure. This has the potential to improve our knowledge of heart failure pathophysiology, identify novel biomarkers of disease, and guide the development of targeted therapeutics for heart failure.

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42

Kouzoupis, Spyros, Andreas Neocleous, and Irene Athanassakis. "Categorization of Mouse Ultrasonic Vocalizations Using Machine Learning Techniques." Acoustics 1, no. 4 (November 4, 2019): 837–46. http://dx.doi.org/10.3390/acoustics1040050.

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A study of the ultrasonic vocalizations of several adult male BALB/c mice in the presence of a female, is undertaken in this study. A total of 179 distinct ultrasonic syllables referred to as “phonemes” are isolated, and in the resulting dataset, k-means and agglomerative clustering algorithms are implemented to group the ultrasonic vocalizations into clusters based on features extracted from their pitch contours. In order to find the optimal number of clusters, the elbow method was used, and nine distinct categories were obtained. Results when the k-means method was applied are presented through a matching matrix, while clustering results when the agglomerative technique was applied are presented as a dendrogram. The results of both methods are in line with the manual annotations made by the authors, as well as with the ones presented in the literature. The two methods of unsupervised analysis applied on 14 element feature vectors provide evidence that vocalizations can be grouped into nine clusters, which translates into the claim that there is a distinct repertoire of “syllables” or “phonemes”.

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43

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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44

Thomas, Elizabeth, Marc M. Van Hulle, and Rufin Vogel. "Encoding of Categories by Noncategory-Specific Neurons in the Inferior Temporal Cortex." Journal of Cognitive Neuroscience 13, no. 2 (February 1, 2001): 190–200. http://dx.doi.org/10.1162/089892901564252.

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In order to understand how the brain codes natural categories, e.g., trees and fish, recordings were made in the anterior part of the macaque inferior temporal (IT) cortex while the animal was performing a tree/nontree categorization task. Most single cells responded to exemplars of more than one category while other neurons responded only to a restricted set of exemplars of a given category. Since it is still not known which type of cells contribute and what is the nature of the code used for categorization in IT, we have performed an analysis on single-cell data. A Kohonen self-organizing map (SOM), which uses an unsupervised (competitive) learning algorithm, was used to study the single cell responses to tree and nontree images. Results from the Kohonen SOM indicated that the collected neuronal data consisting of spike counts was sufficient to account for a good level of categorization success (approximately 83%) when categorizing a group of 200 trees and nontrees. Contrary to intuition, the results of the investigation suggest that the population of category-specific neurons (neurons that respond only to trees or only to nontrees) was unimportant to the categorization. Instead, a large majority of the neurons that were most important to the categorization was found to belong to a class of more broadly tuned cells, namely, cells that responded to both categories but that favored one category over the other by seven or more images. A simple algebraic operation (without the Kohonen SOM) between the above-mentioned noncategory-specific neurons confirmed the contribution of these neurons to categorization. Thus, the modeling results suggest (1) that broadly tuned neurons are critical for categorization, and (2) that only one additional layer of processing is required to extract the categories from a population of IT neurons.

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Toldo, Marco, Andrea Maracani, Umberto Michieli, and Pietro Zanuttigh. "Unsupervised Domain Adaptation in Semantic Segmentation: A Review." Technologies 8, no. 2 (June 21, 2020): 35. http://dx.doi.org/10.3390/technologies8020035.

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The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation. This task is attracting a wide interest since semantic segmentation models require a huge amount of labeled data and the lack of data fitting specific requirements is the main limitation in the deployment of these techniques. This field has been recently explored and has rapidly grown with a large number of ad-hoc approaches. This motivates us to build a comprehensive overview of the proposed methodologies and to provide a clear categorization. In this paper, we start by introducing the problem, its formulation and the various scenarios that can be considered. Then, we introduce the different levels at which adaptation strategies may be applied: namely, at the input (image) level, at the internal features representation and at the output level. Furthermore, we present a detailed overview of the literature in the field, dividing previous methods based on the following (non mutually exclusive) categories: adversarial learning, generative-based, analysis of the classifier discrepancies, self-teaching, entropy minimization, curriculum learning and multi-task learning. Novel research directions are also briefly introduced to give a hint of interesting open problems in the field. Finally, a comparison of the performance of the various methods in the widely used autonomous driving scenario is presented.

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Gourisaria, Mahendra Kumar, Harshvardhan GM, Rakshit Agrawal, Sudhansu Shekhar Patra, Siddharth Swarup Rautaray, and Manjusha Pandey. "Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals." International Journal of E-Health and Medical Communications 12, no. 6 (November 2021): 1–24. http://dx.doi.org/10.4018/ijehmc.20211101.oa9.

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Arrhythmia is a disorder of the heart caused by the erratic nature of heartbeats occurring due to conduction failures of the electrical signals in the cardiac muscle. In recent years, research galore has been done towards accurate categorization of heartbeats and electrocardiogram (ECG)-based heartbeat processing. Accurate categorization of different heartbeats is an important step for diagnosis of arrhythmia. This paper primarily focuses on effective feature extraction of the ECG signals for model performance enhancement using an unsupervised Deep Belief Network (DBN) pipelined onto a simple Logistic Regression (LR) classifier. We compare and evaluate the results of data feature enrichment against plain, non-enriched data based on the metrics of precision, recall, specificity, and F1-score and report the extent of increase in performance. Also, we compare the performance of the DBN-LR pipeline with a 1D convolution technique and find that the DBN-LR algorithm achieves a 5% and 10% increase in accuracy when compared to 1D convolution and no feature extraction using DBN respectively.

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Baraldi, Andrea, and Flavio Parmiggiani. "A neural network for unsupervised categorization of multivalued input patterns: an application to satellite imaee clustering." IEEE Transactions on Geoscience and Remote Sensing 33, no. 2 (March 1995): 305–16. http://dx.doi.org/10.1109/tgrs.1995.8746011.

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Baraldi, A., and F. Parmiggiani. "A neural network for unsupervised categorization of multivalued input patterns: an application to satellite image clustering." IEEE Transactions on Geoscience and Remote Sensing 33, no. 2 (March 1995): 305–16. http://dx.doi.org/10.1109/36.377930.

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Jha, Ashutosh, and Debashis Saha. "Examining categorization of Telecom Circles in India using unsupervised k-means clustering on techno-economic indicators." DECISION 46, no. 4 (November 12, 2019): 365–83. http://dx.doi.org/10.1007/s40622-019-00225-6.

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Aditya Prakash. "Twitter Sentimental Analysis." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 18, 2020): 355–59. http://dx.doi.org/10.46501/ijmtst061266.

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Twitter sentiment analysis (TSA) provides the methods to survey public emotions about the products or events associated with them. Categorization of opinions through tweets involves a great scope of study and may yield interesting results and insights on public opinion and social behavior towards different events, services, product, geopolitical issues, situations and scenarios that concern mankind at large. These attributes are expressed explicitly through emoticons, exclamation, sentiment words and so on. In this paper, we introduce a word embedding (Word2Vec) technique obtained by unsupervised learning built on large twitter corpora, this process uses co-occurrence statistical characteristics between words in tweets and hidden contextual semantic interrelation

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