Gotowa bibliografia na temat „Supervised neural networks”
Utwórz poprawne odniesienie w stylach APA, MLA, Chicago, Harvard i wielu innych
Spis treści
Zobacz listy aktualnych artykułów, książek, rozpraw, streszczeń i innych źródeł naukowych na temat „Supervised neural networks”.
Przycisk „Dodaj do bibliografii” jest dostępny obok każdej pracy w bibliografii. Użyj go – a my automatycznie utworzymy odniesienie bibliograficzne do wybranej pracy w stylu cytowania, którego potrzebujesz: APA, MLA, Harvard, Chicago, Vancouver itp.
Możesz również pobrać pełny tekst publikacji naukowej w formacie „.pdf” i przeczytać adnotację do pracy online, jeśli odpowiednie parametry są dostępne w metadanych.
Artykuły w czasopismach na temat "Supervised neural networks"
Yeh, I.-Cheng, i Kuan-Cheng Lin. "Supervised Learning Probabilistic Neural Networks". Neural Processing Letters 34, nr 2 (22.07.2011): 193–208. http://dx.doi.org/10.1007/s11063-011-9191-z.
Pełny tekst źródłaHush, D. R., i B. G. Horne. "Progress in supervised neural networks". IEEE Signal Processing Magazine 10, nr 1 (styczeń 1993): 8–39. http://dx.doi.org/10.1109/79.180705.
Pełny tekst źródłaTomasov, Adrian, Martin Holik, Vaclav Oujezsky, Tomas Horvath i Petr Munster. "GPON PLOAMd Message Analysis Using Supervised Neural Networks". Applied Sciences 10, nr 22 (18.11.2020): 8139. http://dx.doi.org/10.3390/app10228139.
Pełny tekst źródłaHammer, Barbara. "Neural Smithing – Supervised Learning in Feedforward Artificial Neural Networks". Pattern Analysis & Applications 4, nr 1 (marzec 2001): 73–74. http://dx.doi.org/10.1007/s100440170029.
Pełny tekst źródłaSarukkai, Ramesh R. "Supervised Networks That Self-Organize Class Outputs". Neural Computation 9, nr 3 (1.03.1997): 637–48. http://dx.doi.org/10.1162/neco.1997.9.3.637.
Pełny tekst źródłaDoyle, J. R. "Supervised learning in N-tuple neural networks". International Journal of Man-Machine Studies 33, nr 1 (lipiec 1990): 21–40. http://dx.doi.org/10.1016/s0020-7373(05)80113-0.
Pełny tekst źródłaSecco, Jacopo, Mauro Poggio i Fernando Corinto. "Supervised neural networks with memristor binary synapses". International Journal of Circuit Theory and Applications 46, nr 1 (styczeń 2018): 221–33. http://dx.doi.org/10.1002/cta.2429.
Pełny tekst źródłaSporea, Ioana, i André Grüning. "Supervised Learning in Multilayer Spiking Neural Networks". Neural Computation 25, nr 2 (luty 2013): 473–509. http://dx.doi.org/10.1162/neco_a_00396.
Pełny tekst źródłaWang, Juexin, Anjun Ma, Qin Ma, Dong Xu i 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.
Pełny tekst źródłaXu, Jianqiao, Zhaolu Zuo, Danchao Wu, Bing Li, Xiaoni Li i Deyi Kong. "Bearing Defect Detection with Unsupervised Neural Networks". Shock and Vibration 2021 (19.08.2021): 1–11. http://dx.doi.org/10.1155/2021/9544809.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaGraves, Alex. "Supervised sequence labelling with recurrent neural networks". kostenfrei, 2008. http://mediatum2.ub.tum.de/doc/673554/673554.pdf.
Pełny tekst źródłaWang, Yuxuan. "Supervised Speech Separation Using Deep Neural Networks". The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1426366690.
Pełny tekst źródłaHu, Renjie. "Random neural networks for dimensionality reduction and regularized supervised learning". Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6960.
Pełny tekst źródłaAylas, Victor David Sanchez. "Contributions to Supervised Learning of Real-Valued Functions Using Neural Networks". NSUWorks, 1998. http://nsuworks.nova.edu/gscis_etd/395.
Pełny tekst źródłaTatsumi, 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.
Pełny tekst źródłaVančo, 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.
Pełny tekst źródłaCharles, 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/.
Pełny tekst źródłaTang, Yuxing. "Weakly supervised learning of deformable part models and convolutional neural networks for object detection". Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC062/document.
Pełny tekst źródłaIn 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, i 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.
Pełny tekst źródłaSyftet 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.
Książki na temat "Supervised neural networks"
J, Marks Robert, red. Neural smithing: Supervised learning in feedforward artificial neural networks. Cambridge, Mass: The MIT Press, 1999.
Znajdź pełny tekst źródłaSuresh, Sundaram. Supervised Learning with Complex-valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Znajdź pełny tekst źródłaGraves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Znajdź pełny tekst źródłaGraves, 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.
Pełny tekst źródłaSuresh, Sundaram, Narasimhan Sundararajan i 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.
Pełny tekst źródłaSurinder, Singh. Exploratory spatial data analysis using supervised neural networks. London: University of East London, 1994.
Znajdź pełny tekst źródłaSupervised and unsupervised pattern recognition: Feature extraction and computational intelligence. Boca Raton, Fla: CRC Press, 2000.
Znajdź pełny tekst źródłaSFI/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. Redaktor Wolpert David H. Reading, Mass: Addison-Wesley Pub. Co., 1995.
Znajdź pełny tekst źródłaLeung, 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.
Znajdź pełny tekst źródłaSupervised Learning With Complexvalued Neural Networks. Springer, 2012.
Znajdź pełny tekst źródłaCzęści książek na temat "Supervised neural networks"
Castillo, Oscar, i Patricia Melin. "Supervised Learning Neural Networks". W 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.
Pełny tekst źródłaMelin, Patricia, i Oscar Castillo. "Supervised Learning Neural Networks". W 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.
Pełny tekst źródłaBuscema, Massimo. "Supervised Artificial Neural Networks: Backpropagation Neural Networks". W 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.
Pełny tekst źródłaBehnke, Sven. "Supervised Learning". W 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.
Pełny tekst źródłaHvitfeldt, Emil, i Julia Silge. "Dense neural networks". W 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.
Pełny tekst źródłaHvitfeldt, Emil, i Julia Silge. "Convolutional neural networks". W 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.
Pełny tekst źródłaHammer, Barbara, Alexander Hasenfuss, Frank-Michael Schleif i Thomas Villmann. "Supervised Batch Neural Gas". W Artificial Neural Networks in Pattern Recognition, 33–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11829898_4.
Pełny tekst źródłaBrabazon, Anthony, Michael O’Neill i Seán McGarraghy. "Neural Networks for Supervised Learning". W Natural Computing Algorithms, 221–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_13.
Pełny tekst źródłaFernández-Redondo, Mercedes, Joaquín Torres-Sospedra i Carlos Hernández-Espinosa. "Training RBFs Networks: A Comparison Among Supervised and Not Supervised Algorithms". W Neural Information Processing, 477–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893028_53.
Pełny tekst źródłaHajek, Petr, i Vladimir Olej. "Municipal Creditworthiness Modelling by Kernel-Based Approaches with Supervised and Semi-supervised Learning". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Supervised neural networks"
Hagiwara i Nakagawa. "Supervised learning with artificial selection". W International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118443.
Pełny tekst źródłaBerton, Lilian, Jorge Valverde-Rebaza i Alneu de Andrade Lopes. "Link prediction in graph construction for supervised and semi-supervised learning". W 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280543.
Pełny tekst źródłaYin. "On asymptotic properties of supervised learning". W International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118540.
Pełny tekst źródłaLamba, Sahil, i Rishab Lamba. "Spiking Neural Networks Vs Convolutional Neural Networks for Supervised Learning". W 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2019. http://dx.doi.org/10.1109/icccis48478.2019.8974507.
Pełny tekst źródłaMaggu, Jyoti, i Angshul Majumdar. "Supervised Kernel Transform Learning". W 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8852179.
Pełny tekst źródłaJordanov, Ivan, Nedyalko Petrov i Alessio Petrozziello. "Supervised radar signal classification". W 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727371.
Pełny tekst źródłaWang, Jim Jing-Yan, i Xin Gao. "Semi-supervised sparse coding". W 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889449.
Pełny tekst źródłaShukla, Ankita, Gullal S. Cheema i Saket Anand. "Semi-Supervised Clustering with Neural Networks". W 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM). IEEE, 2020. http://dx.doi.org/10.1109/bigmm50055.2020.00030.
Pełny tekst źródłaHarrison, Kyle, i Amit Kumar Mishra. "Supervised Neural Networks for RFI Flagging". W 2019 RFI Workshop - Coexisting with Radio Frequency Interference (RFI). IEEE, 2019. http://dx.doi.org/10.23919/rfi48793.2019.9111748.
Pełny tekst źródłaZhan, Youqiu. "Self-supervised hamiltonian mechanics neural networks". W 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2021. http://dx.doi.org/10.1109/iccece51280.2021.9342165.
Pełny tekst źródłaRaporty organizacyjne na temat "Supervised neural networks"
Zhang, Yunchong. Blind Denoising by Self-Supervised Neural Networks in Astronomical Datasets (Noise2Self4Astro). Office of Scientific and Technical Information (OSTI), sierpień 2019. http://dx.doi.org/10.2172/1614728.
Pełny tekst źródłaFarhi, Edward, i Hartmut Neven. Classification with Quantum Neural Networks on Near Term Processors. Web of Open Science, grudzień 2020. http://dx.doi.org/10.37686/qrl.v1i2.80.
Pełny tekst źródła