Academic literature on the topic 'Unsupervised neural networks'
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Journal articles on the topic "Unsupervised neural networks"
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.
Full textLuo, 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.
Full textMeuleman, 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.
Full textGunhan, 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.
Full textBecker, 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.
Full textHamad, 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.
Full textVamaraju, 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.
Full textXu, 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.
Full textRaja, 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.
Full textParisi, 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.
Full textDissertations / Theses on the topic "Unsupervised neural networks"
Nyamapfene, Abel. "Unsupervised multimodal neural networks." Thesis, University of Surrey, 2006. http://epubs.surrey.ac.uk/844064/.
Full textMacdonald, 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.
Full textBerry, 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.
Full textHarpur, 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.
Full textWalcott, 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.
Full textVetcha, 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.
Full textBishop, Griffin R. "Unsupervised Semantic Segmentation through Cross-Instance Representation Similarity." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1371.
Full textPlumbley, 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.
Full textGaltier, Mathieu. "A mathematical approach to unsupervised learning in recurrent neural networks." Paris, ENMP, 2011. https://pastel.hal.science/pastel-00667368.
Full textIn this thesis, we propose to give a mathematical sense to the claim: the neocortex builds itself a model of its environment. We study the neocortex as a network of spiking neurons undergoing slow STDP learning. By considering that the number of neurons is close to infinity, we propose a new mean-field method to find the ''smoother'' equation describing the firing-rate of populations of these neurons. Then, we study the dynamics of this averaged system with learning. By assuming the modification of the synapses' strength is very slow compared the activity of the network, it is possible to use tools from temporal averaging theory. They lead to showing that the connectivity of the network always converges towards a single equilibrium point which can be computed explicitely. This connectivity gathers the knowledge of the network about the world. Finally, we analyze the equilibrium connectivity and compare it to the inputs. By seeing the inputs as the solution of a dynamical system, we are able to show that the connectivity embedded the entire information about this dynamical system. Indeed, we show that the symmetric part of the connectivity leads to finding the manifold over which the inputs dynamical system is defined, and that the anti-symmetric part of the connectivity corresponds to the vector field of the inputs dynamical system. In this context, the network acts as a predictor of the future events in its environment
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.
Full textArtificiella neurala nätverk (ANN) har tillämpats på många problem. Däremot försöker inte de flesta ANN-modeller efterlikna hjärnan i detalj. Ett exempel på ett ANN som är begränsat till att efterlikna hjärnan är Hierarchical Temporal Memory (HTM). Denna studie tillämpar HTM och Long Short-Term Memory (LSTM) på avvikelsedetektionsproblem i tidsserier för att undersöka vilka styrkor och svagheter de har för detta problem. Avvikelserna i denna studie är begränsade till punktavvikelser och tidsserierna är i endast en variabel. Redan existerande implementationer som utnyttjar dessa nätverk för oövervakad avvikelsedetektionsproblem i tidsserier används i denna studie. Vi använder främst våra egna syntetiska tidsserier för att undersöka hur nätverken hanterar brus och hur de hanterar olika egenskaper som en tidsserie kan ha. Våra resultat visar att båda nätverken kan hantera brus och prestationsskillnaden rörande brusrobusthet var inte tillräckligt stor för att urskilja modellerna. LSTM presterade bättre än HTM på att upptäcka punktavvikelser i våra syntetiska tidsserier som följer en sinuskurva men en slutsats angående vilket nätverk som presterar bäst överlag är fortfarande oavgjord.
Books on the topic "Unsupervised neural networks"
Baruque, Bruno. Fusion methods for unsupervised learning ensembles. Berlin: Springer, 2010.
Find full textSupervised and unsupervised pattern recognition: Feature extraction and computational intelligence. Boca Raton, Fla: CRC Press, 2000.
Find full textWhitehead, P. A. Design considerations for a hardware accelerator for Kohonen unsupervised learning in artificial neural networks. Manchester: UMIST, 1997.
Find full textSzu, 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.
Find full textSejnowski, Terrence J., Tomaso A. Poggio, and Geoffrey Hinton. Unsupervised Learning: Foundations of Neural Computation. MIT Press, 2016.
Find full textSejnowski, Terrence J., and Geoffrey Hinton. Unsupervised Learning: Foundations of Neural Computation. MIT Press, 1999.
Find full textE, Hinton Geoffrey, and Sejnowski Terrence J, eds. Unsupervised learning: Foundations of neural computation. Cambridge, Mass: MIT Press, 1999.
Find full textBaruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2014.
Find full textBecker, Helen Suzanna. An information-theoretic unsupervised learning algorithm for neural networks. 1993.
Find full text(Editor), Geoffrey Hinton, and Terrence J. Sejnowski (Editor), eds. Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience). The MIT Press, 1999.
Find full textBook chapters on the topic "Unsupervised neural networks"
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.
Full textMüller, 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.
Full textRojas, 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.
Full textCastillo, 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.
Full textMelin, 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.
Full textBehnke, 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.
Full textTrentin, 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.
Full textDonald, 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.
Full textCook, 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.
Full textBrabazon, 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.
Full textConference papers on the topic "Unsupervised neural networks"
Kosko. "Unsupervised learning in noise." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118553.
Full textFang, 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.
Full textDajani, 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.
Full textXiaolong 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.
Full textMiller, 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.
Full textFreisleben, 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.
Full textDe 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.
Full textSun, 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.
Full textSzu, 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.
Full textCerisara, 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.
Full textReports on the topic "Unsupervised neural networks"
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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