Academic literature on the topic 'Supervised neural networks'
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Journal articles on the topic "Supervised neural networks"
Yeh, I.-Cheng, and Kuan-Cheng Lin. "Supervised Learning Probabilistic Neural Networks." Neural Processing Letters 34, no. 2 (July 22, 2011): 193–208. http://dx.doi.org/10.1007/s11063-011-9191-z.
Full textHush, D. R., and B. G. Horne. "Progress in supervised neural networks." IEEE Signal Processing Magazine 10, no. 1 (January 1993): 8–39. http://dx.doi.org/10.1109/79.180705.
Full textTomasov, Adrian, Martin Holik, Vaclav Oujezsky, Tomas Horvath, and Petr Munster. "GPON PLOAMd Message Analysis Using Supervised Neural Networks." Applied Sciences 10, no. 22 (November 18, 2020): 8139. http://dx.doi.org/10.3390/app10228139.
Full textHammer, Barbara. "Neural Smithing – Supervised Learning in Feedforward Artificial Neural Networks." Pattern Analysis & Applications 4, no. 1 (March 2001): 73–74. http://dx.doi.org/10.1007/s100440170029.
Full textSarukkai, Ramesh R. "Supervised Networks That Self-Organize Class Outputs." Neural Computation 9, no. 3 (March 1, 1997): 637–48. http://dx.doi.org/10.1162/neco.1997.9.3.637.
Full textDoyle, J. R. "Supervised learning in N-tuple neural networks." International Journal of Man-Machine Studies 33, no. 1 (July 1990): 21–40. http://dx.doi.org/10.1016/s0020-7373(05)80113-0.
Full textSecco, Jacopo, Mauro Poggio, and Fernando Corinto. "Supervised neural networks with memristor binary synapses." International Journal of Circuit Theory and Applications 46, no. 1 (January 2018): 221–33. http://dx.doi.org/10.1002/cta.2429.
Full textSporea, Ioana, and André Grüning. "Supervised Learning in Multilayer Spiking Neural Networks." Neural Computation 25, no. 2 (February 2013): 473–509. http://dx.doi.org/10.1162/neco_a_00396.
Full textWang, Juexin, Anjun Ma, Qin Ma, Dong Xu, and Trupti Joshi. "Inductive inference of gene regulatory network using supervised and semi-supervised graph neural networks." Computational and Structural Biotechnology Journal 18 (2020): 3335–43. http://dx.doi.org/10.1016/j.csbj.2020.10.022.
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 textDissertations / Theses on the topic "Supervised neural networks"
Sporea, Ioana. "Supervised learning in multilayer spiking neural networks." Thesis, University of Surrey, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.576119.
Full textGraves, Alex. "Supervised sequence labelling with recurrent neural networks." kostenfrei, 2008. http://mediatum2.ub.tum.de/doc/673554/673554.pdf.
Full textWang, Yuxuan. "Supervised Speech Separation Using Deep Neural Networks." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1426366690.
Full textHu, Renjie. "Random neural networks for dimensionality reduction and regularized supervised learning." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6960.
Full textAylas, Victor David Sanchez. "Contributions to Supervised Learning of Real-Valued Functions Using Neural Networks." NSUWorks, 1998. http://nsuworks.nova.edu/gscis_etd/395.
Full textTatsumi, Keiji. "Studies on supervised learning for neural networks with applications to optimization problems." 京都大学 (Kyoto University), 2006. http://hdl.handle.net/2433/136029.
Full textVančo, Timotej. "Self-supervised učení v aplikacích počítačového vidění." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442510.
Full textCharles, Eugene Yougarajah Andrew. "Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks." Thesis, Cardiff University, 2006. http://orca.cf.ac.uk/56168/.
Full textTang, Yuxing. "Weakly supervised learning of deformable part models and convolutional neural networks for object detection." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC062/document.
Full textIn this dissertation we address the problem of weakly supervised object detection, wherein the goal is to recognize and localize objects in weakly-labeled images where object-level annotations are incomplete during training. To this end, we propose two methods which learn two different models for the objects of interest. In our first method, we propose a model enhancing the weakly supervised Deformable Part-based Models (DPMs) by emphasizing the importance of location and size of the initial class-specific root filter. We first compute a candidate pool that represents the potential locations of the object as this root filter estimate, by exploring the generic objectness measurement (region proposals) to combine the most salient regions and “good” region proposals. We then propose learning of the latent class label of each candidate window as a binary classification problem, by training category-specific classifiers used to coarsely classify a candidate window into either a target object or a non-target class. Furthermore, we improve detection by incorporating the contextual information from image classification scores. Finally, we design a flexible enlarging-and-shrinking post-processing procedure to modify the DPMs outputs, which can effectively match the approximate object aspect ratios and further improve final accuracy. Second, we investigate how knowledge about object similarities from both visual and semantic domains can be transferred to adapt an image classifier to an object detector in a semi-supervised setting on a large-scale database, where a subset of object categories are annotated with bounding boxes. We propose to transform deep Convolutional Neural Networks (CNN)-based image-level classifiers into object detectors by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We have evaluated both our approaches extensively on several challenging detection benchmarks, e.g. , PASCAL VOC, ImageNet ILSVRC and Microsoft COCO. Both our approaches compare favorably to the state-of-the-art and show significant improvement over several other recent weakly supervised detection methods
Pehrson, Jakob, and Sara Lindstrand. "Support Unit Classification through Supervised Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281537.
Full textSyftet med artikeln är att utvärdera den påverkan som en klassificeringsmodell kan ha på den interna processen av kundtjänst inom ett stort digitaliserat företag. Chatbotar används allt mer frekvent bland digitala tjänster, även om den generella effekten inte alltid är tydlig. Studien är uppdelad i följande två frågeställningar: (1) Vilken klassificeringsalgoritm bland naive Bayes, logistisk regression, och neurala nätverk kan bäst förutspå den korrekta hjälpen en användare är i behov av och med vilken noggrannhet? Och (2) Vad är effekten på produktivitet och kundnöjdhet för användandet av maskininlärning för sortering av kundbehov? Data samlades från ett stort, digitalt företags interna databas och används sedan i träning och testning med de tre klassificeringsalgoritmerna. Vidare, en enkät skickades ut med fokus på att förstå hur det nuvarande systemet påverkar de berörda arbetarna. Ett första fynd indikerar att neurala nätverk är den mest lämpade modellen för klassificeringen. Däremot, när omfånget och komplexiteten var begränsat presenterade även naive Bayes och logistisk regression tillräckligt. Ett andra fynd av studien är att klassificeringen potentiellt förbättrar produktiviteten givet att baslinjen är mött. Däremot existerar en svårighet i att dra slutsatser om den exakta effekten på kundnöjdhet eftersom det finns många olika aspekter att ta hänsyn till. Likväl finns en god potential i att uppnå en positiv nettoeffekt.
Books on the topic "Supervised neural networks"
J, Marks Robert, ed. Neural smithing: Supervised learning in feedforward artificial neural networks. Cambridge, Mass: The MIT Press, 1999.
Find full textSuresh, Sundaram. Supervised Learning with Complex-valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Find full textGraves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Find full textGraves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24797-2.
Full textSuresh, Sundaram, Narasimhan Sundararajan, and Ramasamy Savitha. Supervised Learning with Complex-valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-29491-4.
Full textSurinder, Singh. Exploratory spatial data analysis using supervised neural networks. London: University of East London, 1994.
Find full textSupervised and unsupervised pattern recognition: Feature extraction and computational intelligence. Boca Raton, Fla: CRC Press, 2000.
Find full textSFI/CNLS Workshop on Formal Approaches to Supervised Learning (1992 Santa Fe, N.M.). The mathematics of generalization: The proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning. Edited by Wolpert David H. Reading, Mass: Addison-Wesley Pub. Co., 1995.
Find full textLeung, Wing Kai. The specification, analysis and metrics of supervised feedforward artificial neural networks for applied science and engineering applications. Birmingham: University of Central England in Birmingham, 2002.
Find full textSupervised Learning With Complexvalued Neural Networks. Springer, 2012.
Find full textBook chapters on the topic "Supervised neural networks"
Castillo, Oscar, and Patricia Melin. "Supervised Learning Neural Networks." In Soft Computing and Fractal Theory for Intelligent Manufacturing, 47–73. Heidelberg: Physica-Verlag HD, 2003. http://dx.doi.org/10.1007/978-3-7908-1766-9_4.
Full textMelin, Patricia, and Oscar Castillo. "Supervised Learning Neural Networks." In Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing, 55–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32378-5_4.
Full textBuscema, Massimo. "Supervised Artificial Neural Networks: Backpropagation Neural Networks." In Intelligent Data Mining in Law Enforcement Analytics, 119–35. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-4914-6_7.
Full textBehnke, Sven. "Supervised Learning." In Hierarchical Neural Networks for Image Interpretation, 111–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45169-3_6.
Full textHvitfeldt, Emil, and Julia Silge. "Dense neural networks." In Supervised Machine Learning for Text Analysis in R, 231–72. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003093459-13.
Full textHvitfeldt, Emil, and Julia Silge. "Convolutional neural networks." In Supervised Machine Learning for Text Analysis in R, 303–42. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003093459-15.
Full textHammer, Barbara, Alexander Hasenfuss, Frank-Michael Schleif, and Thomas Villmann. "Supervised Batch Neural Gas." In Artificial Neural Networks in Pattern Recognition, 33–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11829898_4.
Full textBrabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Neural Networks for Supervised Learning." In Natural Computing Algorithms, 221–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_13.
Full textFernández-Redondo, Mercedes, Joaquín Torres-Sospedra, and Carlos Hernández-Espinosa. "Training RBFs Networks: A Comparison Among Supervised and Not Supervised Algorithms." In Neural Information Processing, 477–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893028_53.
Full textHajek, Petr, and Vladimir Olej. "Municipal Creditworthiness Modelling by Kernel-Based Approaches with Supervised and Semi-supervised Learning." In Engineering Applications of Neural Networks, 35–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03969-0_4.
Full textConference papers on the topic "Supervised neural networks"
Hagiwara and Nakagawa. "Supervised learning with artificial selection." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118443.
Full textBerton, Lilian, Jorge Valverde-Rebaza, and Alneu de Andrade Lopes. "Link prediction in graph construction for supervised and semi-supervised learning." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280543.
Full textYin. "On asymptotic properties of supervised learning." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118540.
Full textLamba, Sahil, and Rishab Lamba. "Spiking Neural Networks Vs Convolutional Neural Networks for Supervised Learning." In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2019. http://dx.doi.org/10.1109/icccis48478.2019.8974507.
Full textMaggu, Jyoti, and Angshul Majumdar. "Supervised Kernel Transform Learning." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852179.
Full textJordanov, Ivan, Nedyalko Petrov, and Alessio Petrozziello. "Supervised radar signal classification." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727371.
Full textWang, Jim Jing-Yan, and Xin Gao. "Semi-supervised sparse coding." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889449.
Full textShukla, Ankita, Gullal S. Cheema, and Saket Anand. "Semi-Supervised Clustering with Neural Networks." In 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM). IEEE, 2020. http://dx.doi.org/10.1109/bigmm50055.2020.00030.
Full textHarrison, Kyle, and Amit Kumar Mishra. "Supervised Neural Networks for RFI Flagging." In 2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI). IEEE, 2019. http://dx.doi.org/10.23919/rfi48793.2019.9111748.
Full textZhan, Youqiu. "Self-supervised hamiltonian mechanics neural networks." In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2021. http://dx.doi.org/10.1109/iccece51280.2021.9342165.
Full textReports on the topic "Supervised neural networks"
Zhang, Yunchong. Blind Denoising by Self-Supervised Neural Networks in Astronomical Datasets (Noise2Self4Astro). Office of Scientific and Technical Information (OSTI), August 2019. http://dx.doi.org/10.2172/1614728.
Full textFarhi, Edward, and Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, December 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.
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