Relevant bibliographies by topics / Unsupervised neural networks

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

Academic literature on the topic 'Unsupervised neural networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Unsupervised neural networks"

1

Murnion, Shane D. "Spatial analysis using unsupervised neural networks." Computers & Geosciences 22, no. 9 (November 1996): 1027–31. http://dx.doi.org/10.1016/s0098-3004(96)00041-6.

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Luo, Shuyue, Shangbo Zhou, Yong Feng, and Jiangan Xie. "Pansharpening via Unsupervised Convolutional Neural Networks." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 4295–310. http://dx.doi.org/10.1109/jstars.2020.3008047.

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Meuleman, J., and C. van Kaam. "UNSUPERVISED IMAGE SEGMENTATION WITH NEURAL NETWORKS." Acta Horticulturae, no. 562 (November 2001): 101–8. http://dx.doi.org/10.17660/actahortic.2001.562.10.

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Gunhan, Atilla E., László P. Csernai, and Jørgen Randrup. "UNSUPERVISED COMPETITIVE LEARNING IN NEURAL NETWORKS." International Journal of Neural Systems 01, no. 02 (January 1989): 177–86. http://dx.doi.org/10.1142/s0129065789000086.

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We study an idealized neural network that may approximate certain neurophysiological features of natural neural systems. The network contains a mutual lateral inhibition and is subjected to unsupervised learning by means of a Hebb-type learning principle. Its learning ability is analysed as a function of the strength of lateral inhibition and the training set.
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Becker, Suzanna. "UNSUPERVISED LEARNING PROCEDURES FOR NEURAL NETWORKS." International Journal of Neural Systems 02, no. 01n02 (January 1991): 17–33. http://dx.doi.org/10.1142/s0129065791000030.

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Supervised learning procedures for neural networks have recently met with considerable success in learning difficult mappings. However, their range of applicability is limited by their poor scaling behavior, lack of biological plausibility, and restriction to problems for which an external teacher is available. A promising alternative is to develop unsupervised learning algorithms which can adaptively learn to encode the statistical regularities of the input patterns, without being told explicitly the correct response for each pattern. In this paper, we describe the major approaches that have
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Hamad, D., C. Firmin, and J. G. Postaire. "Unsupervised pattern classification by neural networks." Mathematics and Computers in Simulation 41, no. 1-2 (June 1996): 109–16. http://dx.doi.org/10.1016/0378-4754(95)00063-1.

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Vamaraju, Janaki, and Mrinal K. Sen. "Unsupervised physics-based neural networks for seismic migration." Interpretation 7, no. 3 (August 1, 2019): SE189—SE200. http://dx.doi.org/10.1190/int-2018-0230.1.

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We have developed a novel framework for combining physics-based forward models and neural networks to advance seismic processing and inversion algorithms. Migration is an effective tool in seismic data processing and imaging. Over the years, the scope of these algorithms has broadened; today, migration is a central step in the seismic data processing workflow. However, no single migration technique is suitable for all kinds of data and all styles of acquisition. There is always a compromise on the accuracy, cost, and flexibility of these algorithms. On the other hand, machine-learning algorith
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Xu, Jianqiao, Zhaolu Zuo, Danchao Wu, Bing Li, Xiaoni Li, and Deyi Kong. "Bearing Defect Detection with Unsupervised Neural Networks." Shock and Vibration 2021 (August 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/9544809.

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Bearings always suffer from surface defects, such as scratches, black spots, and pits. Those surface defects have great effects on the quality and service life of bearings. Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. However, it is difficult to realize automatic detection of bearing surface defects based on data-driven-based deep learning due to few samples data of bearing defects on the actual production line. Sample preprocessin
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Raja, Muhammad Asif Zahoor. "Unsupervised neural networks for solving Troesch's problem." Chinese Physics B 23, no. 1 (January 2014): 018903. http://dx.doi.org/10.1088/1674-1056/23/1/018903.

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Parisi, Daniel R., Marı́a C. Mariani, and Miguel A. Laborde. "Solving differential equations with unsupervised neural networks." Chemical Engineering and Processing: Process Intensification 42, no. 8-9 (August 2003): 715–21. http://dx.doi.org/10.1016/s0255-2701(02)00207-6.

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Dissertations / Theses on the topic "Unsupervised neural networks"

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Nyamapfene, Abel. "Unsupervised multimodal neural networks." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/844064/.

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We extend the in-situ Hebbian-linked SOMs network by Miikkulainen to come up with two unsupervised neural networks that learn the mapping between the individual modes of a multimodal dataset. The first network, the single-pass Hebbian linked SOMs network, extends the in-situ Hebbian-linked SOMs network by enabling the Hebbian link weights to be computed through one- shot learning. The second network, a modified counter propagation network, extends the unsupervised learning of crossmodal mappings by making it possible for only one self-organising map to implement the crossmodal mapping. The two
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Macdonald, Donald. "Unsupervised neural networks for visualisation of data." Thesis, University of the West of Scotland, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395687.

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Berry, Ian Michael. "Data classification using unsupervised artificial neural networks." Thesis, University of Sussex, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390079.

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Harpur, George Francis. "Low entropy coding with unsupervised neural networks." Thesis, University of Cambridge, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.627227.

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Walcott, Terry Hugh. "Market prediction for SMEs using unsupervised neural networks." Thesis, University of East London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.532991.

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The objective of this study was to create a market prediction model for small and medium enterprises (SMEs). To achieve this, an extensive literature examination was carried out which focused on SMEs, marketing and prediction; neural networks as a competitive tool for SME marketing; and clustering a review. A Delphi study was used for collating expert opinions in order to determine likely factors hindering SMEs wanting to remain business proficient. An analysis of Delphi responses led to the creation of a market prediction questionnaire. This questionnaire was used to create variables for anal
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Vetcha, Sarat Babu. "Fault diagnosis in pumps by unsupervised neural networks." Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300604.

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Bishop, Griffin R. "Unsupervised Semantic Segmentation through Cross-Instance Representation Similarity." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1371.

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Semantic segmentation methods using deep neural networks typically require huge volumes of annotated data to train properly. Due to the expense of collecting these pixel-level dataset annotations, the problem of semantic segmentation without ground-truth labels has been recently proposed. Many current approaches to unsupervised semantic segmentation frame the problem as a pixel clustering task, and in particular focus heavily on color differences between image regions. In this paper, we explore a weakness to this approach: By focusing on color, these approaches do not adequately capture relati
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Plumbley, Mark David. "An information-theoretic approach to unsupervised connectionist models." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387051.

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Galtier, Mathieu. "A mathematical approach to unsupervised learning in recurrent neural networks." Paris, ENMP, 2011. https://pastel.hal.science/pastel-00667368.

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Dans cette thèse nous tentons de donner un sens mathématique à la proposition : le néocortex se construit un modèle de son environnement. Nous considérons que le néocortex est un réseau de neurones spikants dont la connectivité est soumise à une lente évolution appelée apprentissage. Dans le cas où le nombre de neurones est proche de l'infini, nous proposons une nouvelle méthode de champ-moyen afin de trouver une équation décrivant l'évolution du taux de décharge de populations de neurones. Nous étudions donc la dynamique de ce système moyennisé avec apprentissage. Dans le régime où l'apprenti
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Haddad, Josef, and Carl Piehl. "Unsupervised anomaly detection in time series with recurrent neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259655.

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Artificial neural networks (ANN) have been successfully applied to a wide range of problems. However, most of the ANN-based models do not attempt to model the brain in detail, but there are still some models that do. An example of a biologically constrained ANN is Hierarchical Temporal Memory (HTM). This study applies HTM and Long Short-Term Memory (LSTM) to anomaly detection problems in time series in order to compare their performance for this task. The shape of the anomalies are restricted to point anomalies and the time series are univariate. Pre-existing implementations that utilise these
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Books on the topic "Unsupervised neural networks"

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

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Supervised and unsupervised pattern recognition: Feature extraction and computational intelligence. Boca Raton, Fla: CRC Press, 2000.

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Whitehead, P. A. Design considerations for a hardware accelerator for Kohonen unsupervised learning in artificial neural networks. Manchester: UMIST, 1997.

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Szu, Harold H., and Jack Agee. Independent component analyses, wavelets, unsupervised nano-biomimetic sensors, and neural networks VI: 17-19 March 2008, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2008.

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Sejnowski, Terrence J., Tomaso A. Poggio, and Geoffrey Hinton. Unsupervised Learning: Foundations of Neural Computation. MIT Press, 2016.

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Sejnowski, Terrence J., and Geoffrey Hinton. Unsupervised Learning: Foundations of Neural Computation. MIT Press, 1999.

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E, Hinton Geoffrey, and Sejnowski Terrence J, eds. Unsupervised learning: Foundations of neural computation. Cambridge, Mass: MIT Press, 1999.

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Baruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2014.

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Becker, Helen Suzanna. An information-theoretic unsupervised learning algorithm for neural networks. 1993.

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(Editor), Geoffrey Hinton, and Terrence J. Sejnowski (Editor), eds. Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience). The MIT Press, 1999.

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Book chapters on the topic "Unsupervised neural networks"

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Müller, Berndt, and Joachim Reinhardt. "Unsupervised Learning." In Neural Networks, 132–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3_14.

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Müller, Berndt, Joachim Reinhardt, and Michael T. Strickland. "Unsupervised Learning." In Neural Networks, 162–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4_15.

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Rojas, Raúl. "Unsupervised Learning and Clustering Algorithms." In Neural Networks, 99–121. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4_5.

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Castillo, Oscar, and Patricia Melin. "Unsupervised Learning Neural Networks." In Soft Computing and Fractal Theory for Intelligent Manufacturing, 75–92. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1766-9_5.

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Melin, Patricia, and Oscar Castillo. "Unsupervised Learning Neural Networks." In Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing, 85–107. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32378-5_5.

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Behnke, Sven. "Unsupervised Learning." In Hierarchical Neural Networks for Image Interpretation, 95–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45169-3_5.

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Trentin, Edmondo, and Marco Bongini. "Probabilistically Grounded Unsupervised Training of Neural Networks." In Unsupervised Learning Algorithms, 533–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_18.

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Donald, James, and Lex A. Akers. "An Unsupervised Neural Processor." In Silicon Implementation of Pulse Coded Neural Networks, 263–90. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2680-3_12.

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Cook, Matthew, Florian Jug, Christoph Krautz, and Angelika Steger. "Unsupervised Learning of Relations." In Artificial Neural Networks – ICANN 2010, 164–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15819-3_21.

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Brabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Neural Networks for Unsupervised Learning." In Natural Computing Algorithms, 261–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_14.

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Conference papers on the topic "Unsupervised neural networks"

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Kosko. "Unsupervised learning in noise." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118553.

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Fang, L., A. Jennings, W. X. Wen, K. Q. Q. Li, and T. Li. "Unsupervised learning for neural trees." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170278.

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Dajani, A. L., M. Kamel, and M. I. Elmasry. "Gradient methods in unsupervised neural networks." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170685.

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Xiaolong Wang and Chandra Kambhamettu. "Age estimation via unsupervised neural networks." In 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). IEEE, 2015. http://dx.doi.org/10.1109/fg.2015.7163119.

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Miller, Mitchell, Megan Washburn, and Foaad Khosmood. "Evolving unsupervised neural networks for Slither.io." In FDG '19: The Fourteenth International Conference on the Foundations of Digital Games. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3337722.3341837.

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Freisleben, Bernd, and Claudia Hagen. "Unsupervised Hebbian learning in neural networks." In The first international conference on computing anticipatory systems. AIP, 1998. http://dx.doi.org/10.1063/1.56326.

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De Meulemeester, Hannes, and Bart De Moor. "Unsupervised Embeddings for Categorical Variables." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207703.

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Sun, Jianyong, and Aimin Zhou. "Unsupervised robust Bayesian feature selection." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889514.

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Szu, Harold. "Theories of Neural Networks Leading to Unsupervised Learning." In International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371458.

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Cerisara, Christophe, Paul Caillon, and Guillaume Le Berre. "Unsupervised Post-Tuning of Deep Neural Networks." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534198.

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Reports on the topic "Unsupervised neural networks"

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Chavez, Wesley. An Exploration of Linear Classifiers for Unsupervised Spiking Neural Networks with Event-Driven Data. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6323.

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