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Journal articles on the topic 'Unsupervised and supervised learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 22 February 2023

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1

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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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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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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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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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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Sun, Jinghan, Dong Wei, Kai Ma, Liansheng Wang, and Yefeng Zheng. "Boost Supervised Pretraining for Visual Transfer Learning: Implications of Self-Supervised Contrastive Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (2022): 2307–15. http://dx.doi.org/10.1609/aaai.v36i2.20129.

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Unsupervised pretraining based on contrastive learning has made significant progress recently and showed comparable or even superior transfer learning performance to traditional supervised pretraining on various tasks. In this work, we first empirically investigate when and why unsupervised pretraining surpasses supervised counterparts for image classification tasks with a series of control experiments. Besides the commonly used accuracy, we further analyze the results qualitatively with the class activation maps and assess the learned representations quantitatively with the representation ent

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C A Padmanabha Reddy, Y., P. Viswanath, and B. Eswara Reddy. "Semi-supervised learning: a brief review." International Journal of Engineering & Technology 7, no. 1.8 (2018): 81. http://dx.doi.org/10.14419/ijet.v7i1.8.9977.

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Most of the application domain suffers from not having sufficient labeled data whereas unlabeled data is available cheaply. To get labeled instances, it is very difficult because experienced domain experts are required to label the unlabeled data patterns. Semi-supervised learning addresses this problem and act as a half way between supervised and unsupervised learning. This paper addresses few techniques of Semi-supervised learning (SSL) such as self-training, co-training, multi-view learning, TSVMs methods. Traditionally SSL is classified in to Semi-supervised Classification and Semi-supervi

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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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Hui, Binyuan, Pengfei Zhu, and Qinghua Hu. "Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4215–22. http://dx.doi.org/10.1609/aaai.v34i04.5843.

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Graph convolutional networks (GCN) have achieved promising performance in attributed graph clustering and semi-supervised node classification because it is capable of modeling complex graphical structure, and jointly learning both features and relations of nodes. Inspired by the success of unsupervised learning in the training of deep models, we wonder whether graph-based unsupervised learning can collaboratively boost the performance of semi-supervised learning. In this paper, we propose a multi-task graph learning model, called collaborative graph convolutional networks (CGCN). CGCN is compo

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Akdemir, Deniz, and Jean-Luc Jannink. "Ensemble learning with trees and rules: Supervised, semi-supervised, unsupervised." Intelligent Data Analysis 18, no. 5 (2014): 857–72. http://dx.doi.org/10.3233/ida-140672.

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Goernitz, N., M. Kloft, K. Rieck, and U. Brefeld. "Toward Supervised Anomaly Detection." Journal of Artificial Intelligence Research 46 (February 20, 2013): 235–62. http://dx.doi.org/10.1613/jair.3623.

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Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to gr

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Wang, Xiaobin, Deng Cai, Linlin Li, Guangwei Xu, Hai Zhao, and Luo Si. "Unsupervised Learning Helps Supervised Neural Word Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7200–7207. http://dx.doi.org/10.1609/aaai.v33i01.33017200.

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By exploiting unlabeled data for further performance improvement for Chinese word segmentation, this work makes the first attempt at exploring adding unsupervised segmentation information into neural supervised segmenter. We survey various effective strategies, including extending the character embedding, augmenting the word score and applying multi-task learning, for leveraging unsupervised information derived from abundant unlabeled data. Experiments on standard data sets show that the explored strategies indeed improve the recall rate of out-of-vocabulary words and thus boost the segmentati

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Ling, Ping, Nan Jiang, and Xiangsheng Rong. "Integrating the Supervised Information into Unsupervised Learning." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/597521.

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This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs) firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label no

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Gao Huang, Shiji Song, Jatinder N. D. Gupta, and Cheng Wu. "Semi-Supervised and Unsupervised Extreme Learning Machines." IEEE Transactions on Cybernetics 44, no. 12 (2014): 2405–17. http://dx.doi.org/10.1109/tcyb.2014.2307349.

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Mao, Xiangke, Hui Yang, Shaobin Huang, Ye Liu, and Rongsheng Li. "Extractive summarization using supervised and unsupervised learning." Expert Systems with Applications 133 (November 2019): 173–81. http://dx.doi.org/10.1016/j.eswa.2019.05.011.

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Silva, Hugo, and Jorge Bernardino. "Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems." Algorithms 15, no. 4 (2022): 130. http://dx.doi.org/10.3390/a15040130.

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Decision support systems with machine learning can help organizations improve operations and lower costs with more precision and efficiency. This work presents a review of state-of-the-art machine learning algorithms for binary classification and makes a comparison of the related metrics between them with their application to a public diabetes and human resource datasets. The two mainly used categories that allow the learning process without requiring explicit programming are supervised and unsupervised learning. For that, we use Scikit-learn, the free software machine learning library for Pyt

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Abijono, Heri, Puput Santoso, and Novita Lestari Anggreini. "ALGORITMA SUPERVISED LEARNING DAN UNSUPERVISED LEARNING DALAM PENGOLAHAN DATA." Jurnal Teknologi Terapan: G-Tech 4, no. 2 (2021): 315–18. http://dx.doi.org/10.33379/gtech.v4i2.635.

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Seiring dengan zaman yang semakin berkembang seperti saat ini, kini kita berada pada zaman yang mana teknologi menjadi satu hal yang paling penting dan tidak pernah terlepas dari kehidupan manusia. Dengan teknologi informasi dan komunikasi yang ada sekarang ini, semakin memudahkan kita untuk melakukan segala aktifitas. Dengan semakin berkembangnya teknologi informasi juga menghasilkan begitu banyak data yang dapat diolah, sehingga banyak informasi yang tidak terbuang sia-sia. Machine learning dapat digunakan sebagai sistem pengolahan data sehingga dapat mempermudah pengguna dalam mengolah info

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Michaels, Ronald. "Associative Memory with Uncorrelated Inputs." Neural Computation 8, no. 2 (1996): 256–59. http://dx.doi.org/10.1162/neco.1996.8.2.256.

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In hybrid learning schemes a layer of unsupervised learning is followed by supervised learning. In this situation a connection between two unsupervised learning algorithms, principal component analysis and decorrelation, and a supervised learning algorithm, associative memory, is shown. When associative memory is preceded by principal component analysis or decorrelation it is possible to take advantage of the lack of correlation among inputs to associative memory to show that correlation matrix memory is a least squares solution to the supervised learning problem.

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Mukherjee, Prosenjit, Shibaprasad Sen, Kaushik Roy, and Ram Sarkar. "Recognition of Online Handwritten Bangla Characters Using Supervised and Unsupervised Learning Approaches." International Journal of Computer Vision and Image Processing 10, no. 3 (2020): 18–30. http://dx.doi.org/10.4018/ijcvip.2020070102.

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This paper explores the domain of online handwritten Bangla character recognition by stroke-based approach. The component strokes of a character sample are recognized firstly and then characters are constructed from the recognized strokes. In the current experiment, strokes are recognized by both supervised and unsupervised approaches. To estimate the features, images of all the component strokes are superimposed. A mean structure has been generated from this superimposed image. Euclidian distances between pixel points of a stroke sample and mean stroke structure are considered as features. Fo

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Yin, Xinxin, Feng Liu, Run Cai, et al. "Research on Seismic Signal Analysis Based on Machine Learning." Applied Sciences 12, no. 16 (2022): 8389. http://dx.doi.org/10.3390/app12168389.

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In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, is only a one-dimensional convolutional neural network structure which has achieved the best comprehensive results, and its computational efficiency is far stronger than other supervised classification methods. Through our experimental results, we found that the MiniRo

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Aversa, Rossella, Piero Coronica, Cristiano De Nobili, and Stefano Cozzini. "Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification." Data Intelligence 2, no. 4 (2020): 513–28. http://dx.doi.org/10.1162/dint_a_00062.

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In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales (from

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Gureckis, Todd M., and Bradley C. Love. "Human Unsupervised and Supervised Learning as a Quantitative Distinction." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 05 (2003): 885–901. http://dx.doi.org/10.1142/s0218001403002587.

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SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a network model of human category learning. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g. it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes/attractors/rules. SUSTAIN has expanded the scope of findings that models of human category le

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Retnoningsih, Endang, and Rully Pramudita. "Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python." BINA INSANI ICT JOURNAL 7, no. 2 (2020): 156. http://dx.doi.org/10.51211/biict.v7i2.1422.

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Abstrak: Machine learning merupakan sistem yang mampu belajar sendiri untuk memutuskan sesuatu tanpa harus berulangkali diprogram oleh manusia sehingga komputer menjadi semakin cerdas berlajar dari pengalaman data yang dimiliki. Berdasarkan teknik pembelajarannya, dapat dibedakan supervised learning menggunakan dataset (data training) yang sudah berlabel, sedangkan unsupervised learning menarik kesimpulan berdasarkan dataset. Input berupa dataset digunakan pembelajaran mesin untuk menghasilkan analisis yang benar. Permasalahan yang akan diselesaikan bunga iris (iris tectorum) yang memiliki bun

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Wu, Guile, Xiatian Zhu, and Shaogang Gong. "Tracklet Self-Supervised Learning for Unsupervised Person Re-Identification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12362–69. http://dx.doi.org/10.1609/aaai.v34i07.6921.

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Existing unsupervised person re-identification (re-id) methods mainly focus on cross-domain adaptation or one-shot learning. Although they are more scalable than the supervised learning counterparts, relying on a relevant labelled source domain or one labelled tracklet per person initialisation still restricts their scalability in real-world deployments. To alleviate these problems, some recent studies develop unsupervised tracklet association and bottom-up image clustering methods, but they still rely on explicit camera annotation or merely utilise suboptimal global clustering. In this work,

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Song, Yide. "Weakly-Supervised and Unsupervised Video Anomaly Detection." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 160–70. http://dx.doi.org/10.54097/hset.v12i.1444.

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As surveillance technology is continuously improving, an ever-increasing number of cameras are being deployed everywhere. Relying on manual detection of anomalies through cameras may be unreliable and untimely. Therefore, the application of deep learning in video anomaly detection is being extensively studied. Anomaly Detection (AD) refers to identifying events that deviate from the desired actions. This article discusses representative unsupervised and weakly-supervised learning methods applied to various data types. In these machine learning methods, Generative Adversarial Network, Auto Enco

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Khalaf Hamoud, Alaa, Mohammed Baqr Mohammed Kamel, Alaa Sahl Gaafar, et al. "A prediction model based machine learning algorithms with feature selection approaches over imbalanced dataset." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 2 (2022): 1105. http://dx.doi.org/10.11591/ijeecs.v28.i2.pp1105-1116.

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The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' performance data are imbalanced, where the final classes are not equally represented. Besides the size of the dataset, this problem affects the model's prediction accuracy. In this paper, the Synthetic Minority Oversampling Technique (SMOTE) filter is applied to the dataset to find its effect on the model's accuracy. Four feature selection approaches are applied to find the most correlated attributes that affect the students' per

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Sarma, Abhijat, Rupak Chatterjee, Kaitlin Gili, and Ting Yu. "Quantum unsupervised and supervised learning on superconducting processors." Quantum Information and Computation 20, no. 7&8 (2020): 541–52. http://dx.doi.org/10.26421/qic20.7-8-1.

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Machine learning algorithms perform well on identifying patterns in many different datasets due to their versatility. However, as one increases the size of the data, the computation time for training and using these statistical models grows quickly. Here, we propose and implement on the IBMQ a quantum analogue to K-means clustering, and compare it to a previously developed quantum support vector machine. We find the algorithm's accuracy comparable to the classical K-means algorithm for clustering and classification problems, and find that it becomes less computationally expensive to implement

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Bijari, Kayvan, Gema Valera, Hernán López-Schier, and Giorgio A. Ascoli. "Quantitative neuronal morphometry by supervised and unsupervised learning." STAR Protocols 2, no. 4 (2021): 100867. http://dx.doi.org/10.1016/j.xpro.2021.100867.

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Goudbeek, Martijn, Daniel Swingley, and Roel Smits. "Supervised and unsupervised learning of multidimensional acoustic categories." Journal of Experimental Psychology: Human Perception and Performance 35, no. 6 (2009): 1913–33. http://dx.doi.org/10.1037/a0015781.

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Lin, Yi-Nan, Tsang-Yen Hsieh, Cheng-Ying Yang, Victor RL Shen, Tony Tong-Ying Juang, and Wen-Hao Chen. "Deep Petri nets of unsupervised and supervised learning." Measurement and Control 53, no. 7-8 (2020): 1267–77. http://dx.doi.org/10.1177/0020294020923375.

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Artificial intelligence is one of the hottest research topics in computer science. In general, when it comes to the needs to perform deep learning, the most intuitive and unique implementation method is to use neural network. But there are two shortcomings in neural network. First, it is not easy to be understood. When encountering the needs for implementation, it often requires a lot of relevant research efforts to implement the neural network. Second, the structure is complex. When constructing a perfect learning structure, in order to achieve the fully defined connection between nodes, the

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Shen, Victor R. L., Yue-Shan Chang, and Tony Tong-Ying Juang. "Supervised and Unsupervised Learning by Using Petri Nets." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 40, no. 2 (2010): 363–75. http://dx.doi.org/10.1109/tsmca.2009.2038068.

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Corsini, Paolo, Beatrice Lazzerini, and Francesco Marcelloni. "Combining supervised and unsupervised learning for data clustering." Neural Computing and Applications 15, no. 3-4 (2006): 289–97. http://dx.doi.org/10.1007/s00521-006-0030-5.

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Andras, Peter. "Function Approximation Using Combined Unsupervised and Supervised Learning." IEEE Transactions on Neural Networks and Learning Systems 25, no. 3 (2014): 495–505. http://dx.doi.org/10.1109/tnnls.2013.2276044.

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Shamsin, M., N. Krilova, M. Bazhanova, V. Kazantsev, V. A. Makarov, and S. Lobov. "Supervised and unsupervised learning in processing myographic patterns." Journal of Physics: Conference Series 1117 (November 2018): 012008. http://dx.doi.org/10.1088/1742-6596/1117/1/012008.

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Intrator, Nathan. "On the combination of supervised and unsupervised learning." Physica A: Statistical Mechanics and its Applications 200, no. 1-4 (1993): 655–61. http://dx.doi.org/10.1016/0378-4371(93)90572-l.

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Sasakawa, Takafumi, Jinglu Hu, and Kotaro Hirasawa. "A brainlike learning system with supervised, unsupervised, and reinforcement learning." Electrical Engineering in Japan 162, no. 1 (2007): 32–39. http://dx.doi.org/10.1002/eej.20600.

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Quenu, Mathieu, Steven A. Trewick, Fabrice Brescia, and Mary Morgan-Richards. "Geometric morphometrics and machine learning challenge currently accepted species limits of the land snail Placostylus (Pulmonata: Bothriembryontidae) on the Isle of Pines, New Caledonia." Journal of Molluscan Studies 86, no. 1 (2020): 35–41. http://dx.doi.org/10.1093/mollus/eyz031.

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Abstract Size and shape variations of shells can be used to identify natural phenotypic clusters and thus delimit snail species. Here, we apply both supervised and unsupervised machine learning algorithms to a geometric morphometric dataset to investigate size and shape variations of the shells of the endemic land snail Placostylus from New Caledonia. We sampled eight populations of Placostylus from the Isle of Pines, where two species of this genus reportedly coexist. We used neural network analysis as a supervised learning algorithm and Gaussian mixture models as an unsupervised learning alg

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Nadal, J. P., and N. Parga. "Duality Between Learning Machines: A Bridge Between Supervised and Unsupervised Learning." Neural Computation 6, no. 3 (1994): 491–508. http://dx.doi.org/10.1162/neco.1994.6.3.491.

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We exhibit a duality between two perceptrons that allows us to compare the theoretical analysis of supervised and unsupervised learning tasks. The first perceptron has one output and is asked to learn a classification of p patterns. The second (dual) perceptron has p outputs and is asked to transmit as much information as possible on a distribution of inputs. We show in particular that the maximum information that can be stored in the couplings for the supervised learning task is equal to the maximum information that can be transmitted by the dual perceptron.

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Weinlichová, Jana, and Jiří Fejfar. "Usage of self-organizing neural networks in evaluation of consumer behaviour." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no. 6 (2010): 625–32. http://dx.doi.org/10.11118/actaun201058060625.

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This article deals with evaluation of consumer data by Artificial Intelligence methods. In methodical part there are described learning algorithms for Kohonen maps on the principle of supervised learning, unsupervised learning and semi-supervised learning. The principles of supervised learning and unsupervised learning are compared. On base of binding conditions of these principles there is pointed out an advantage of semi-supervised learning. Three algorithms are described for the semi-supervised learning: label propagation, self-training and co-training. Especially usage of co-training in Ko

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Yin, Yuan, Min Jiang Liao, and Xiao Lin Li. "Pedestrian Detection Based on Multi-Stage Unsupervised Learning." Applied Mechanics and Materials 687-691 (November 2014): 957–60. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.957.

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In order to implement effective detection and utilize large numbers of unlabeled samples,a pedestrian detection method based on Unsupervised learning was presented.We apply deep learning to human detection to acquire pedestrian features with unlabeled data set.The detection method uses unsupervised convolution sparse auto-encoders to train features at all levels from the data set,then trains classifier with end-to-end supervised method.Additionally,we fine-tune the features in a supervised way.Experiments show that the method approach an state-of-art result on all data set.

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Xu, Shaoping, Xiaojun Chen, Yiling Tang, Shunliang Jiang, Xiaohui Cheng, and Nan Xiao. "Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior." Applied Sciences 12, no. 21 (2022): 10767. http://dx.doi.org/10.3390/app122110767.

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Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the training data. Recently, several unsupervised learning models have been proposed to reduce the dependence on training data. Although unsupervised methods only require noisy images for learning, their denoising effect is relatively weak compared with supervised methods. This paper proposes a two-stage unsupervised deep learning framework based on d

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Liu, Chenghua, Zhuolin Liao, Yixuan Ma, and Kun Zhan. "Stationary Diffusion State Neural Estimation for Multiview Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7542–49. http://dx.doi.org/10.1609/aaai.v36i7.20719.

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Although many graph-based clustering methods attempt to model the stationary diffusion state in their objectives, their performance limits to using a predefined graph. We argue that the estimation of the stationary diffusion state can be achieved by gradient descent over neural networks. We specifically design the Stationary Diffusion State Neural Estimation (SDSNE) to exploit multiview structural graph information for co-supervised learning. We explore how to design a graph neural network specially for unsupervised multiview learning and integrate multiple graphs into a unified consensus grap

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Tseng, Shao-Yen, Brian Baucom, and Panayiotis Georgiou. "Unsupervised online multitask learning of behavioral sentence embeddings." PeerJ Computer Science 5 (June 10, 2019): e200. http://dx.doi.org/10.7717/peerj-cs.200.

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Appropriate embedding transformation of sentences can aid in downstream tasks such as NLP and emotion and behavior analysis. Such efforts evolved from word vectors which were trained in an unsupervised manner using large-scale corpora. Recent research, however, has shown that sentence embeddings trained using in-domain data or supervised techniques, often through multitask learning, perform better than unsupervised ones. Representations have also been shown to be applicable in multiple tasks, especially when training incorporates multiple information sources. In this work we aspire to combine

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Hsu, Chia-Yi, Pin-Yu Chen, Songtao Lu, Sijia Liu, and Chia-Mu Yu. "Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6926–34. http://dx.doi.org/10.1609/aaai.v36i6.20650.

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Adversarial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. However, current studies focus on supervised learning tasks, relying on the ground truth data label, a targeted objective, or supervision from a trained classifier. In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation. Our framework exploits a mutual information neural estimator as an information theoretic similarity measure to generate adversarial examples without

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Yamkovyi, Klym. "DEVELOPMENT AND COMPARATIVE ANALYSIS OF SEMI-SUPERVISED LEARNING ALGORITHMS ON A SMALL AMOUNT OF LABELED DATA." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (5) (July 12, 2021): 98–103. http://dx.doi.org/10.20998/2079-0023.2021.01.16.

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The paper is dedicated to the development and comparative experimental analysis of semi-supervised learning approaches based on a mix of unsupervised and supervised approaches for the classification of datasets with a small amount of labeled data, namely, identifying to which of a set of categories a new observation belongs using a training set of data containing observations whose category membership is known. Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Unlabeled data, when used

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Guo, Wenbin, and Juan Zhang. "Semi-supervised learning for raindrop removal on a single image." Journal of Intelligent & Fuzzy Systems 42, no. 4 (2022): 4041–49. http://dx.doi.org/10.3233/jifs-212342.

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This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised netw

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Kim, Sungil, Byungjoon Yoon, Jung-Tek Lim, and Myungsun Kim. "Data-Driven Signal–Noise Classification for Microseismic Data Using Machine Learning." Energies 14, no. 5 (2021): 1499. http://dx.doi.org/10.3390/en14051499.

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It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal–noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly

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Zhou, Meng, Zechen Li, and Pengtao Xie. "Self-supervised Regularization for Text Classification." Transactions of the Association for Computational Linguistics 9 (2021): 641–56. http://dx.doi.org/10.1162/tacl_a_00389.

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Abstract Text classification is a widely studied problem and has broad applications. In many real-world problems, the number of texts for training classification models is limited, which renders these models prone to overfitting. To address this problem, we propose SSL-Reg, a data-dependent regularization approach based on self-supervised learning (SSL). SSL (Devlin et al., 2019a) is an unsupervised learning approach that defines auxiliary tasks on input data without using any human-provided labels and learns data representations by solving these auxiliary tasks. In SSL-Reg, a supervised class

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