Relevant bibliographies by topics / Supervised and unsupervised learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Supervised and unsupervised learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 June 2025

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Journal articles on the topic "Supervised and unsupervised 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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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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Liu, MengYang, MingJun Li, and XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents." Computational Intelligence and Neuroscience 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.

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Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no s
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Liu, MengYang, MingJun Li, and XiaoYang Zhang. "The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents." Computational Intelligence and Neuroscience 2022 (June 6, 2022): 1–10. http://dx.doi.org/10.1155/2022/5653942.

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Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate on unsupervised learning methodology. The two important models are as follows: the supervised and unsupervised models. The main difference is the method of training that they undergo. Supervised models are provided with training on a particular dataset and its outcome. In the case of unsupervised models, only input data is given, and there is no s
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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. http://dx.doi.org/10.11591/ijeecs.v27.i2.pp1016-1024.

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This research aims to <span lang="EN-US">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 val
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Sharma, Ritu. "Study of Supervised Learning and Unsupervised Learning." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (2020): 588–93. http://dx.doi.org/10.22214/ijraset.2020.6095.

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Ezadeen Mehyadin, Aska, and Adnan Mohsin Abdulazeez. "CLASSIFICATION BASED ON SEMI-SUPERVISED LEARNING: A REVIEW." Iraqi Journal for Computers and Informatics 47, no. 1 (2021): 1–11. http://dx.doi.org/10.25195/ijci.v47i1.277.

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Semi-supervised learning is the class of machine learning that deals with the use of supervised and unsupervised learning to implement the learning process. Conceptually placed between labelled and unlabeled data. In certain cases, it enables the large numbers of unlabeled data required to be utilized in comparison with usually limited collections of labeled data. In standard classification methods in machine learning, only a labeled collection is used to train the classifier. In addition, labelled instances are difficult to acquire since they necessitate the assistance of annotators, who serv
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Love, Bradley C. "Comparing supervised and unsupervised category learning." Psychonomic Bulletin & Review 9, no. 4 (2002): 829–35. http://dx.doi.org/10.3758/bf03196342.

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Liu, Jianran, Chan Li, and Wenyuan Yang. "Supervised Learning via Unsupervised Sparse Autoencoder." IEEE Access 6 (2018): 73802–14. http://dx.doi.org/10.1109/access.2018.2884697.

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Amrita, Sadarangani *. Dr. Anjali Jivani. "A SURVEY OF SEMI-SUPERVISED LEARNING." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 10 (2016): 138–43. https://doi.org/10.5281/zenodo.159333.

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Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classification, and unsupervised clustering and semi-supervised clustering are also discussed.
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Dissertations / Theses on the topic "Supervised and unsupervised learning"

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Tsang, Wai-Hung. "Kernel methods in supervised and unsupervised learning /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20TSANG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 46-49). Also available in electronic version. Access restricted to campus users.
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Aversano, Gianmarco. "Development of physics-based reduced-order models for reacting flow applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC095/document.

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L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées et combinées dans les travaux de la présente thèse pour l’extraction de caractéristiques et la construction de modèles d’ordre réduit. Ainsi, l’application de techniques pilotées par les données pour la détection des caractéristiques d’ensembles de données de combustion turbulente (simulation numérique directe) a été étudiée sur deux flammes H2 / CO: une évolution spatiale (DNS1) et une jet à évolution tem
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Hasenjäger, Martina. "Active data selection in supervised and unsupervised learning." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=960209220.

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Mansinghka, Vikash Kumar. "Nonparametric Bayesian methods for supervised and unsupervised learning." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53172.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Includes bibliographical references (leaves 44-45).<br>I introduce two nonparametric Bayesian methods for solving problems of supervised and unsupervised learning. The first method simultaneously learns causal networks and causal theories from data. For example, given synthetic co-occurrence data from a simple causal model for the medical domain, it can learn relationships like "having a flu causes coughing", while also learning that observable quantities can be usefully grou
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Sîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.

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La direction de recherche que nous abordons dans la thèse est l'application des modèles dynamiques d'apprentissage automatique pour résoudre les problèmes de classification supervisée et non supervisée. Les problèmes particuliers que nous avons décidé d'aborder dans la thèse sont la reconnaissance des piétons (un problème de classification supervisée) et le groupement des données d'expression génétique (un problème de classification non supervisée). Les problèmes abordés sont représentatifs pour les deux principaux types de classification et sont très difficiles, ayant une grande importance da
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Bass, Gideon. "Ensemble supervised and unsupervised learning with Kepler variable stars." Thesis, George Mason University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10027479.

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<p>Variable star analysis and classification is an important task in the understanding of stellar features and processes. While historically classifications have been done manually by highly skilled experts, the recent and rapid expansion in the quantity and quality of data has demanded new techniques, most notably automatic classification through supervised machine learning. I present a study on variable stars in the Kepler field using these techniques, and the novel work of unsupervised learning. I use new methods of characterization and multiple independent classifiers to produce an ensembl
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Hess, Andreas. "Supervised and unsupervised ensemble learning for the semantic web." [Mainz] [A. Hess], 2006. http://d-nb.info/99714971X/34.

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Liu, Dongnan. "Supervised and Unsupervised Deep Learning-based Biomedical Image Segmentation." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/24744.

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Biomedical image analysis plays a crucial role in the development of healthcare, with a wide scope of applications including the disease diagnosis, clinical treatment, and future prognosis. Among various biomedical image analysis techniques, segmentation is an essential step, which aims at assigning each pixel with labels of interest on the category and instance. At the early stage, the segmentation results were obtained via manual annotation, which is time-consuming and error-prone. Over the past few decades, hand-craft feature based methods have been proposed to segment the biomedical images
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Nallabolu, Adithya Reddy. "Unsupervised Learning of Spatiotemporal Features by Video Completion." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/79702.

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In this work, we present an unsupervised representation learning approach for learning rich spatiotemporal features from videos without the supervision from semantic labels. We propose to learn the spatiotemporal features by training a 3D convolutional neural network (CNN) using video completion as a surrogate task. Using a large collection of unlabeled videos, we train the CNN to predict the missing pixels of a spatiotemporal hole given the remaining parts of the video through minimizing per-pixel reconstruction loss. To achieve good reconstruction results using color videos, the CNN needs to
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Nasrin, Mst Shamima. "Pathological Image Analysis with Supervised and Unsupervised Deep Learning Approaches." University of Dayton / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1620052562772676.

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Books on the topic "Supervised and unsupervised learning"

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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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Berry, Michael W., Azlinah Mohamed, and Bee Wah Yap, eds. Supervised and Unsupervised Learning for Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-22475-2.

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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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Ros, Frédéric, and Serge Guillaume, eds. Sampling Techniques for Supervised or Unsupervised Tasks. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-29349-9.

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Okun, Oleg, and Giorgio Valentini, eds. Applications of Supervised and Unsupervised Ensemble Methods. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03999-7.

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Acuña, Ana Isabel González. Contributions to unsupervised and supervised learning with applications in digital image processing: Dissertation presented to the Department Of Computer Science and Artificial Intelligence in partial fulfillment of the requeriments for the degree of Doctor of Philosophy. Universidad del País Vasco, Servicio Editorial = Euskal Herriko Unibertsitatea, Argitalpen Zerbitzua, 2012.

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Okun, Oleg, and Giorgio Valentini, eds. Supervised and Unsupervised Ensemble Methods and their Applications. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78981-9.

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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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Schwenker, Friedhelm, and Edmondo Trentin, eds. Partially Supervised Learning. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28258-4.

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Zhou, Zhi-Hua, and Friedhelm Schwenker, eds. Partially Supervised Learning. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40705-5.

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Book chapters on the topic "Supervised and unsupervised learning"

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Taguchi, Y.-h. "PCA Based Unsupervised FE." In Unsupervised and Semi-Supervised Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22456-1_4.

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Taguchi, Y.-h. "TD Based Unsupervised FE." In Unsupervised and Semi-Supervised Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22456-1_5.

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Taguchi, Y.-h. "TD-Based Unsupervised FE." In Unsupervised and Semi-Supervised Learning. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60982-4_5.

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Taguchi, Y.-h. "PCA-Based Unsupervised FE." In Unsupervised and Semi-Supervised Learning. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-60982-4_4.

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Ciotola, Matteo, and Giuseppe Scarpa. "Unsupervised Pansharpening Using ConvNets." In Unsupervised and Semi-Supervised Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68106-6_7.

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Lai, Tze Leung, and Haipeng Xing. "Supervised and unsupervised learning." In Data Science and Risk Analytics in Finance and Insurance. CRC Press, 2024. http://dx.doi.org/10.1201/9781315117041-5.

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Campos Zabala, Francisco Javier. "Supervised and Unsupervised Learning." In Grow Your Business with AI. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9669-1_9.

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Ros, Frederic, and Rabia Riad. "Learning approaches and tricks." In Unsupervised and Semi-Supervised Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48743-9_7.

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Ros, Frederic, and Rabia Riad. "Chapter 6: Deep learning architectures." In Unsupervised and Semi-Supervised Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-48743-9_6.

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M. Bagirov, Adil, Napsu Karmitsa, and Sona Taheri. "Introduction to Clustering." In Unsupervised and Semi-Supervised Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37826-4_1.

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Conference papers on the topic "Supervised and unsupervised learning"

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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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R, Jeyalakshmi, Namita Rajput, and S. Helen Roselin Gracy. "Financial Fraud detection using Supervised and Unsupervised Learning." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780301.

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Zheng, JunShuai, YiChao Zhou, XiYuan Hu, and ZhenMin Tang. "Deepfake Detection With Combined Unsupervised-Supervised Contrastive Learning." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647603.

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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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Agrawal, Shashwat, Gopal Kumar Gupta, Pandi Kirupa Gopalakrishna, Vanitha Sivasankaran Balasubramaniam, Lagan Goel, and Siddhey Mahadik. "Hybrid Machine Learning Models: Combining Strengths of Supervised and Unsupervised Learning Approaches." In 2024 7th International Conference on Contemporary Computing and Informatics (IC3I). IEEE, 2024. https://doi.org/10.1109/ic3i61595.2024.10829140.

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Pucoe, Gloria, and Ibidun Christiana Obagbuwa. "Wine Quality Prediction Using Supervised and Unsupervised Machine Learning Techniques." In 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG). IEEE, 2024. http://dx.doi.org/10.1109/seb4sdg60871.2024.10629999.

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Manzoor, Bisma, and Akram Al-Hourani. "Joint Supervised and Unsupervised Machine Learning for Spaceborne Spectrum Sensing." In 2024 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE). IEEE, 2024. https://doi.org/10.1109/wisee61249.2024.10850453.

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Aleksić, Veljko. "Unsupervised and Semi-Supervised Learning Techniques in Contemporary Educational Application." In Sinteza 2025. Singidunum University, 2025. https://doi.org/10.15308/sinteza-2025-259-266.

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Levi, Gil. "Connecting Supervised and Unsupervised Sentence Embeddings." In Proceedings of The Third Workshop on Representation Learning for NLP. Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-3010.

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Inoue, Tomoya, Yujin Nakagawa, Ryota Wada, et al. "Early Stuck Detection Using Supervised and Unsupervised Machine Learning Approaches." In Offshore Technology Conference Asia. OTC, 2022. http://dx.doi.org/10.4043/31376-ms.

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Abstract The early detection of a stuck pipe event is crucial as it is one of the major incidents resulting in nonproductive time. An ordinary supervised machine learning approach has been adopted to achieve the detection of stuck pipe in some previous studies. However, for early detection before stuck occurs with this approach, there are challenging issues such as limited stuck pipe data, various causes of stuck, and the lack of a prior exact "stuck sign" which should be a label in the training dataset. In this study, the surface drilling data is first collected from multiple agencies to enha
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Reports on the topic "Supervised and unsupervised learning"

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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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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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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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Lin, Youzuo. Physics-guided Machine Learning: from Supervised Deep Networks to Unsupervised Lightweight Models. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1994110.

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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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Tran, Anh, Theron Rodgers, and Timothy Wildey. Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1673174.

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Hodgdon, Taylor, Anthony Fuentes, Jason Olivier, Brian Quinn, and Sally Shoop. Automated terrain classification for vehicle mobility in off-road conditions. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40219.

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The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be in-formed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collec
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Estrella, Tony, Carla Alfonso, Lluis Capdevila, and Josep-Maria Losilla. Machine learning for the analysis of healthy lifestyle data: a scoping review protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.3.0065.

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Review question / Objective: The objective of this scoping review is to identify and characterize machine learning algorithms used in data analysis of healthy lifestyle. The specific objectives are the study of a) terminology, b) healthy lifestyle variables analysed either input or output, c) programs and libraries used to analyse data, and d) sources, types, and quality of data analysed. Eligibility criteria: In this scoping review the inclusion criteria from studies that provide empirical information are as follows: a) studies must use machine learning models either supervised or unsupervise
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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, 2023. http://dx.doi.org/10.3289/sw_2_2023.

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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in
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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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