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Relevant bibliographies by topics / Unsupervied learning

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Academic literature on the topic 'Unsupervied learning'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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Journal articles on the topic "Unsupervied learning"

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Fong, A. C. M., and G. Hong. "Boosted Supervised Intensional Learning Supported by Unsupervised Learning." International Journal of Machine Learning and Computing 11, no. 2 (2021): 98–102. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1020.

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Traditionally, supervised machine learning (ML) algorithms rely heavily on large sets of annotated data. This is especially true for deep learning (DL) neural networks, which need huge annotated data sets for good performance. However, large volumes of annotated data are not always readily available. In addition, some of the best performing ML and DL algorithms lack explainability – it is often difficult even for domain experts to interpret the results. This is an important consideration especially in safety-critical applications, such as AI-assisted medical endeavors, in which a DL’s failure

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Xu, Mingle, Sook Yoon, Jaesu Lee, and Dong Sun Park. "Unsupervised Transfer Learning for Plant Anomaly Recognition." Korean Institute of Smart Media 11, no. 4 (2022): 30–37. http://dx.doi.org/10.30693/smj.2022.11.4.30.

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Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this

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Hu Haofeng, 胡浩丰, 金慧烽 Jin Huifeng, 李校博 Li Xiaobo, 翟京生 Zhai Jingsheng та 刘铁根 Liu Tiegen. "基于无监督学习的偏振图像去噪方法". Acta Optica Sinica 43, № 4 (2023): 0410001. http://dx.doi.org/10.3788/aos221645.

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A Sowe, Ebou. "Momentum Contrast for Unsupervised Visual Representation Learning." Journal of Advances in Civil and Mechanical Engineering 2, no. 1 (2025): 01–06. https://doi.org/10.64030/3067-2457.02.01.02.

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This brief report presents a novel unsupervised learning representation learning method called momentum contrast. Momentum contrast uses a contrastive learning technique to learn representations by comparing features of related yet dissimilar images for efficient feature extraction and unsupervised representation learning. Similar images are grouped together, and dissimilar images are placed far apart. The method builds upon previous works in contrastive learning but includes a momentum optimisation step to improve representation learning performance and generate better quality representations

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Kruglov, Artem V. "The Unsupervised Learning Algorithm for Detecting Ellipsoid Objects." International Journal of Machine Learning and Computing 9, no. 3 (2019): 255–60. http://dx.doi.org/10.18178/ijmlc.2019.9.3.795.

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Yu-Dong Cao, Yu-Dong Cao, Shuang-Jiang Hang Yu-Dong Cao, and Xu Jia Shuang-Jiang Hang. "Improving Unsupervised Domain Adaptation via Multiple Adversarial Learning." 電腦學刊 34, no. 5 (2023): 073–85. http://dx.doi.org/10.53106/199115992023103405006.

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<p>Most machine learning methods assume the training and test sets to be independent and have identical distributions. However, this assumption does not always hold true in practical applications. Direct training usually induces poor performance if the training and test data have distribution shifts. To address this issue, a three-part model based on using a feature extractor, a classifier, and several domain discriminators is adopted herein. This unsupervised domain adaptation model is based on multiple adversarial learning with samples of different importance. A deep neural network is

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Shi, Chengming, Bo Luo, Hongqi Li, Bin Li, Xinyong Mao, and Fangyu Peng. "Anomaly Detection via Unsupervised Learning for Tool Breakage Monitoring." International Journal of Machine Learning and Computing 6, no. 5 (2016): 256–59. http://dx.doi.org/10.18178/ijmlc.2016.6.5.607.

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S Thakare Jayshri, Vishal. "An Effective Unsupervised Machine Learning Technique and Research Challenges." International Journal of Science and Research (IJSR) 12, no. 5 (2023): 2141–43. http://dx.doi.org/10.21275/sr23523214829.

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Lok, Lai Kai, Vazeerudeen Abdul Hameed, and Muhammad Ehsan Rana. "Hybrid machine learning approach for anomaly detection." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 2 (2022): 1016–24. https://doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to improve anomaly detection performance by developing two variants of hybrid models combining supervised and unsupervised machine learning techniques. Supervised models cannot detect new or unseen types of anomaly. Hence in variant 1, a supervised model that detects normal samples is followed by an unsupervised learning model to screen anomaly. The unsupervised model is weak in differentiating between noise and fraud. Hence in variant 2, the hybrid model incorporates an unsupervised model that detects anomaly is followed by a supervised model to validate an anomaly. Three d

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Barlow, H. B. "Unsupervised Learning." Neural Computation 1, no. 3 (1989): 295–311. http://dx.doi.org/10.1162/neco.1989.1.3.295.

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What use can the brain make of the massive flow of sensory information that occurs without any associated rewards or punishments? This question is reviewed in the light of connectionist models of unsupervised learning and some older ideas, namely the cognitive maps and working models of Tolman and Craik, and the idea that redundancy is important for understanding perception (Attneave 1954), the physiology of sensory pathways (Barlow 1959), and pattern recognition (Watanabe 1960). It is argued that (1) The redundancy of sensory messages provides the knowledge incorporated in the maps or models.

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Dissertations / Theses on the topic "Unsupervied learning"

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GIOBERGIA, FLAVIO. "Machine learning with limited label availability: algorithms and applications." Doctoral thesis, Politecnico di Torino, 2023. https://hdl.handle.net/11583/2976594.

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Snyder, Benjamin Ph D. Massachusetts Institute of Technology. "Unsupervised multilingual learning." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62455.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 241-254).<br>For centuries, scholars have explored the deep links among human languages. In this thesis, we present a class of probabilistic models that exploit these links as a form of naturally occurring supervision. These models allow us to substantially improve performance for core text processing tasks, such as morphological segmentation, part-of-speech tagging, and syntactic parsing. Besides

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Geigel, Arturo. "Unsupervised Learning Trojan." NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/17.

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This work presents a proof of concept of an Unsupervised Learning Trojan. The Unsupervised Learning Trojan presents new challenges over previous work on the Neural network Trojan, since the attacker does not control most of the environment. The current work will presented an analysis of how the attack can be successful by proposing new assumptions under which the attack can become a viable one. A general analysis of how the compromise can be theoretically supported is presented, providing enough background for practical implementation development. The analysis was carried out using 3 selected

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Mathieu, Michael. "Unsupervised Learning under Uncertainty." Thesis, New York University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10261120.

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<p> Deep learning, in particular neural networks, achieved remarkable success in the recent years. However, most of it is based on supervised learning, and relies on ever larger datasets, and immense computing power. One step towards general artificial intelligence is to build a model of the world, with enough knowledge to acquire a kind of ``common sense''. Representations learned by such a model could be reused in a number of other tasks. It would reduce the requirement for labelled samples and possibly acquire a deeper understanding of the problem. The vast quantities of knowledge required

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Boschini, Matteo. "Unsupervised Learning of Scene Flow." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16226/.

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As Computer Vision-powered autonomous systems are increasingly deployed to solve problems in the wild, the case is made for developing visual understanding methods that are robust and flexible. One of the most challenging tasks for this purpose is given by the extraction of scene flow, that is the dense three-dimensional vector field that associates each world point with its corresponding position in the next observed frame, hence describing its three-dimensional motion entirely. The recent addition of a limited amount of ground truth scene flow information to the popular KITTI dataset prompt

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Jelacic, Mersad. "Unsupervised Learning for Plant Recognition." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-247.

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<p>Six methods are used for clustering data containing two different objects: sugar-beet plants </p><p>and weed. These objects are described by 19 different features, i.e. shape and color features. </p><p>There is also information about the distance between sugar-beet plants that is used for </p><p>labeling clusters. The methods that are evaluated: k-means, k-medoids, hierarchical clustering, </p><p>competitive learning, self-organizing maps and fuzzy c-means. After using the methods on </p><p>plant data, clusters are formed. The clusters are labeled with three different proposed </p><p>method

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Amin, Khizer, and Mehmood ul haq Minhas. "Facebook Blocket with Unsupervised Learning." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1969.

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The Internet has become a valuable channel for both business-to- consumer and business-to-business e-commerce. It has changed the way for many companies to manage the business. Every day, more and more companies are making their presence on Internet. Web sites are launched for online shopping as web shops or on-line stores are a popular means of goods distribution. The number of items sold through the internet has sprung up significantly in the past few years. Moreover, it has become a choice for customers to do shopping at their ease. Thus, the aim of this thesis is to design and implement a

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Korkontzelos, Ioannis. "Unsupervised learning of multiword expressions." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/2091/.

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Multiword expressions are expressions consisting of two or more words that correspond to some conventional way of saying things (Manning & Schutze 1999). Due to the idiomatic nature of many of them and their high frequency of occurence in all sorts of text, they cause problems in many Natural Language Processing (NLP) applications and are frequently responsible for their shortcomings. Efficiently recognising multiword expressions and deciding the degree of their idiomaticity would be useful to all applications that require some degree of semantic processing, such as question-answering, summari

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Liang, Yingyu. "Modern aspects of unsupervised learning." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52282.

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Unsupervised learning has become more and more important due to the recent explosion of data. Clustering, a key topic in unsupervised learning, is a well-studied task arising in many applications ranging from computer vision to computational biology to the social sciences. This thesis is a collection of work exploring two modern aspects of clustering: stability and scalability. In the first part, we study clustering under a stability property called perturbation resilience. As an alternative approach to worst case analysis, this novel theoretical framework aims at understanding the complexity

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Xiao, Ying. "New tools for unsupervised learning." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52995.

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In an unsupervised learning problem, one is given an unlabelled dataset and hopes to find some hidden structure; the prototypical example is clustering similar data. Such problems often arise in machine learning and statistics, but also in signal processing, theoretical computer science, and any number of quantitative scientific fields. The distinguishing feature of unsupervised learning is that there are no privileged variables or labels which are particularly informative, and thus the greatest challenge is often to differentiate between what is relevant or irrelevant in any particular datase

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Books on the topic "Unsupervied learning"

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Kyan, Matthew, Paisarn Muneesawang, Kambiz Jarrah, and Ling Guan. Unsupervised Learning. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118875568.

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Celebi, M. Emre, and Kemal Aydin, eds. Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8.

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Li, Xiangtao, and Ka-Chun Wong, eds. Natural Computing for Unsupervised Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98566-4.

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Leordeanu, Marius. Unsupervised Learning in Space and Time. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42128-1.

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Baruque, Bruno, and Emilio Corchado. Fusion Methods for Unsupervised Learning Ensembles. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16205-3.

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Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unsupervised Machine Learning. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557693.

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Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1637-8.

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Bartlett, Marian Stewart. Face image analysis by unsupervised learning. Kluwer Academic Publishers, 2001.

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Baruque, Bruno. Fusion methods for unsupervised learning ensembles. Springer, 2010.

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Bartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Springer US, 2001.

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Book chapters on the topic "Unsupervied learning"

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Deepak, P. "Anomaly Detection for Data with Spatial Attributes." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_1.

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Torra, Vicenç, Guillermo Navarro-Arribas, and Klara Stokes. "An Overview of the Use of Clustering for Data Privacy." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_10.

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Wang, Chang-Dong, and Jian-Huang Lai. "Nonlinear Clustering: Methods and Applications." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_11.

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İnkaya, Tülin, Sinan Kayalıgil, and Nur Evin Özdemirel. "Swarm Intelligence-Based Clustering Algorithms: A Survey." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_12.

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Huang, Xiaohui, Yunming Ye, and Haijun Zhang. "Extending Kmeans-Type Algorithms by Integrating Intra-cluster Compactness and Inter-cluster Separation." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_13.

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Tsolakis, Dimitrios M., and George E. Tsekouras. "A Fuzzy-Soft Competitive Learning Approach for Grayscale Image Compression." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_14.

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Wong, Ka-Chun, Yue Li, and Zhaolei Zhang. "Unsupervised Learning in Genome Informatics." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_15.

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Martin, Dian I., John C. Martin, and Michael W. Berry. "The Application of LSA to the Evaluation of Questionnaire Responses." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_16.

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Ahmed, Rezwan, and George Karypis. "Mining Evolving Patterns in Dynamic Relational Networks." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_17.

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Trentin, Edmondo, and Marco Bongini. "Probabilistically Grounded Unsupervised Training of Neural Networks." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_18.

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Conference papers on the topic "Unsupervied learning"

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Li, Xi, Disha Biswas, Peng Zhou, Wesley H. Brigner, Joseph S. Friedman, and Qing Gu. "Experimental Validation of Online Learning in Deep Photonic Neural Networks." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jth2a.85.

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We experimentally demonstrated supervised and unsupervised online learning for the “NCSUTD” letter recognition task in a deep photonic neural network using fiber optics and proposed a chip-scale crossbar multilayer structure for unsupervised learning.

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Shashidhar, Sumuk, Abhinav Chinta, Vaibhav Sahai, and Dilek Hakkani Tur. "Unsupervised Human Preference Learning." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.200.

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Hossain, Mazharul, Aaron L. Robinson, Lan Wang, and Chrysanthe Preza. "Investigation of unsupervised and supervised hyperspectral anomaly detection." In Applications of Machine Learning 2024, edited by Barath Narayanan, Michael E. Zelinski, Tarek M. Taha, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2024. http://dx.doi.org/10.1117/12.3029916.

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Pedro, Kevin. "Searching for Strongly Coupled Dark Sectors with Unsupervised and Generative Learning." In Searching for Strongly Coupled Dark Sectors with Unsupervised and Generative Learning. US DOE, 2024. http://dx.doi.org/10.2172/2429224.

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Yu, Francis T. S., Taiwei Lu, and Don A. Gregory. "Self-Learning Optical Neural Network." In Spatial Light Modulators and Applications. Optica Publishing Group, 1990. http://dx.doi.org/10.1364/slma.1990.mb4.

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One of the features in neural computing must be the adaptability to changeable environment and to recognize unknown objects. In general, there are two types of learning processes that are used in the human brain; supervised and unsupervised learnings [1]. In a supervised learning process, the artificial neural network has to be taught when to learn and when to process the information. Nevertheless, if an unknown object is presented to the artificial neural network during the processing, the network may provide an error output result. On the other hand, for unsupervised learning (also called se

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Xu, Huang, Trieu Phat Luu, Guodong David Zhan, et al. "Physics-Guided Data Augmentation Combined with Unsupervised Learning Improves Stability and Accuracy of Bit Wear Deep Learning Model." In IADC/SPE International Drilling Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/217954-ms.

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Abstract Data is one of the most important limiting factors of deep machine learning (ML) model in drilling applications. Though a big size of historical data can be available, high-quality cleaned and labeled data is usually limited. In this case study, we show that with limited labeled data, physics-based data augmentation combined with unsupervised learning significantly improves both stability and accuracy in bit wear ML model. It provides a pathway to overcome labeled data shortage and field data quality limitations. Labeled bit wear data is usually limited because only the final bit dull

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Shui, Xinghua, and Huadong Zheng. "Multi-depth Hologram Generation with Unsupervised-learning Based Computer-generated Holography." In Digital Holography and Three-Dimensional Imaging. Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.w5a.12.

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Unsupervised-learning based computer-generated holography provides an approach for 2D hologram generation. We propose an unsupervised learning network for multi-depth hologram generation with fully utilizing the different representations of multi-depth object.

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Ver Steeg, Greg. "Unsupervised Learning via Total Correlation Explanation." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/740.

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Learning by children and animals occurs effortlessly and largely without obvious supervision. Successes in automating supervised learning have not translated to the more ambiguous realm of unsupervised learning where goals and labels are not provided. Barlow (1961) suggested that the signal that brains leverage for unsupervised learning is dependence, or redundancy, in the sensory environment. Dependence can be characterized using the information-theoretic multivariate mutual information measure called total correlation. The principle of Total Cor-relation Ex-planation (CorEx) is to learn repr

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Gonzalez, Andres, Zoya Heidari, and Olivier Lopez. "Data-Driven Algorithms for Image-Based Rock Classification and Formation Evaluation in Formations With Rapid Spatial Variation in Rock Fabric." In 2022 SPWLA 63rd Annual Symposium. Society of Petrophysicists and Well Log Analysts, 2022. http://dx.doi.org/10.30632/spwla-2022-0018.

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Supervised learning algorithms can be employed for automation of time-intensive tasks, such as image-based rock classification. However, labeled data is not always available. Alternatively, unsupervised learning algorithms, which do not require labeled data, can be employed. Using either of these methods depends on the evaluated formations and the available training/input data sets. Therefore, further investigation is needed to compare the performance of both approaches. The objectives of this paper include, (a) to train two supervised learning models for image-based rock classification employ

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Reite, Aaron A., Scott Kangas, Zackery Steck, George S. Goley, Jonathan Von Stroh, and Steven Forsyth. "Unsupervised feature learning in remote sensing." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2529791.

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Reports on the topic "Unsupervied learning"

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Vesselinov, Velimir Valentinov. TensorDecompostions : Unsupervised machine learning methods. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1493534.

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Moral, Rafael. Introduction to Machine Learning. Instats Inc., 2024. http://dx.doi.org/10.61700/qfxukp14jlpfd1478.

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This comprehensive workshop provides a thorough introduction to machine learning, focusing on both theoretical concepts and practical applications using R. Designed for PhD students, professors, and researchers, it covers essential techniques such as supervised and unsupervised learning, dimension reduction, and tree-based methods, enhancing participants' data analysis skills and research capabilities.

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Sprechmann, Pablo, and Guillermo Sapiro. Dictionary Learning and Sparse Coding for Unsupervised Clustering. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada513140.

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Vesselinov, Velimir, Bulbul Ahmmed, Maruti Mudunuru, et al. Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1781347.

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Safta, Cosmin, Habib Najm, Michael Grant, and Michael Sparapany. Trajectory Optimization via Unsupervised Probabilistic Learning On Manifolds. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1821958.

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Bhattarai, Manish. UNSUPERVISED AND SUPERVISED LEARNING FRAMEWORKS FOR KNOWLEDGE EXTRACTION. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2342020.

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Shekhar, Shubhranshu, Jetson Leder-Luis, and Leman Akoglu. Unsupervised Machine Learning for Explainable Health Care Fraud Detection. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w30946.

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Fessel, Kimberly. Machine Learning Essentials (Free Seminar). Instats Inc., 2024. http://dx.doi.org/10.61700/l6x4izy1bov9p1764.

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This comprehensive one-hour seminar provides PhD students, academics, and professional researchers with fundamental insights into machine learning concepts, crucial for modern data analysis in many disciplines. Led by data science expert Dr Kimberly Fessel, participants will explore key topics such as supervised and unsupervised learning, model performance (under- vs. overfitting), and popular algorithms like linear and logistic regression, decision trees, and neural networks.

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Ahmmed, Bulbul. Supervised and Unsupervised Machine Learning to Understanding Reactive-transport Data. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1630844.

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Obert, James, and Timothy James Loffredo. Efficient Binary Static Code Data Flow Analysis Using Unsupervised Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1592974.

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