Gotowe bibliografie tematyczne / Supervised neural networks

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Gotowa bibliografia na temat „Supervised neural networks”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 1 lutego 2022

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Artykuły w czasopismach na temat "Supervised neural networks"

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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.

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Hush, 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.

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Tomasov, 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.

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This paper discusses the possibility of analyzing the orchestration protocol used in gigabit-capable passive optical networks (GPONs). Considering the fact that a GPON is defined by the International Telecommunication Union Telecommunication sector (ITU-T) as a set of recommendations, implementation across device vendors might exhibit few differences, which complicates analysis of such protocols. Therefore, machine learning techniques are used (e.g., neural networks) to evaluate differences in GPONs among various device vendors. As a result, this paper compares three neural network models base
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Hammer, 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.

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Sarukkai, 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.

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Supervised, neural network, learning algorithms have proved very successful at solving a variety of learning problems; however, they suffer from a common problem of requiring explicit output labels. In this article, it is shown that pattern classification can be achieved, in a multilayered, feedforward, neural network, without requiring explicit output labels, by a process of supervised self-organization. The class projection is achieved by optimizing appropriate within-class uniformity and between-class discernibility criteria. The mapping function and the class labels are developed together
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Doyle, 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.

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Secco, 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.

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Sporea, 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.

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We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algorithm is applied successfully to classic linearly nonseparable benchmarks such as the XOR problem and the Iris data set, as well as to more complex classification and mapping problems. The algorithm has b
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Wang, 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.

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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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Rozprawy doktorskie na temat "Supervised neural networks"

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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.

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In this thesis, a new supervised learning algorithm for multilayer spik- ing neural networks is presented. Gradient descent learning algo- rithms have led traditional neural networks with multiple layers to be one of the most powerful and flexible computational models derived from artificial neural networks. However, more recent experimental evidence suggests that biological neural systems use the exact time of single action potentials to encode information. These findings have led to a new way of simulating neural networks based on temporal en- coding with single spikes. Analytical demonstrat
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Graves, Alex. "Supervised sequence labelling with recurrent neural networks." kostenfrei, 2008. http://mediatum2.ub.tum.de/doc/673554/673554.pdf.

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Wang, Yuxuan. "Supervised Speech Separation Using Deep Neural Networks." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1426366690.

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Hu, Renjie. "Random neural networks for dimensionality reduction and regularized supervised learning." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/6960.

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This dissertation explores Random Neural Networks (RNNs) in several aspects and their applications. First, Novel RNNs have been proposed for dimensionality reduction and visualization. Based on Extreme Learning Machines (ELMs) and Self-Organizing Maps (SOMs) a new method is created to identify the important variables and visualize the data. This technique reduces the curse of dimensionality and improves furthermore the interpretability of the visualization and is tested on real nursing survey datasets. ELM-SOM+ is an autoencoder created to preserves the intrinsic quality of SOM and also brings
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Aylas, Victor David Sanchez. "Contributions to Supervised Learning of Real-Valued Functions Using Neural Networks." NSUWorks, 1998. http://nsuworks.nova.edu/gscis_etd/395.

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This dissertation presents a new strategy for the automatic design of neural networks. The learning environment addressed is supervised learning from examples. Specifically, Radial Basis Functions (RBF) networks learning real-valued functions of real vectors as in non-linear regression applications are considered. The strategy is based upon the application of strong theoretical relationships between RBF networks and methods from approximation theory, robust statistics, and computational learning theory. The complexity of the network design is examined in detail from the formal definition of th
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Tatsumi, 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.

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Vančo, 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.

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The aim of the diploma thesis is to make research of the self-supervised learning in computer vision applications, then to choose a suitable test task with an extensive data set, apply self-supervised methods and evaluate. The theoretical part of the work is focused on the description of methods in computer vision, a detailed description of neural and convolution networks and an extensive explanation and division of self-supervised methods. Conclusion of the theoretical part is devoted to practical applications of the Self-supervised methods in practice. The practical part of the diploma thesi
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Charles, 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/.

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Artificial neural networks are learning paradigms which mimic the biological neural system. The temporal coding Spiking Neural Network, a relatively new artificial neural network paradigm, is considered to be computationally more powerful than the conventional neural network. Research on the network of spiking neurons is an emerging field and has potential for wider investigation. This research explores alternative learning models with temporal coding spiking neural networks for clustering and classification tasks. Neurons are known to be operating in two modes namely, as integrators and coinc
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Tang, Yuxing. "Weakly supervised learning of deformable part models and convolutional neural networks for object detection." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC062/document.

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Dans cette thèse, nous nous intéressons au problème de la détection d’objets faiblement supervisée. Le but est de reconnaître et de localiser des objets dans les images, n’ayant à notre disposition durant la phase d’apprentissage que des images partiellement annotées au niveau des objets. Pour cela, nous avons proposé deux méthodes basées sur des modèles différents. Pour la première méthode, nous avons proposé une amélioration de l’approche ”Deformable Part-based Models” (DPM) faiblement supervisée, en insistant sur l’importance de la position et de la taille du filtre racine initial spécifiqu
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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.

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The purpose of this article is to evaluate the impact a supervised machine learning classification model can have on the process of internal customer support within a large digitized company. Chatbots are becoming a frequently used utility among digital services, though the true general impact is not always clear. The research is separated into the following two questions: (1) Which supervised machine learning algorithm of naïve Bayes, logistic regression, and neural networks can best predict the correct support a user needs and with what accuracy? And (2) What is the effect on the productivit
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Książki na temat "Supervised neural networks"

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J, Marks Robert, ed. Neural smithing: Supervised learning in feedforward artificial neural networks. Cambridge, Mass: The MIT Press, 1999.

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Suresh, Sundaram. Supervised Learning with Complex-valued Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Graves, 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.

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Suresh, 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.

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Surinder, Singh. Exploratory spatial data analysis using supervised neural networks. London: University of East London, 1994.

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

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SFI/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.

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Leung, 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.

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Supervised Learning With Complexvalued Neural Networks. Springer, 2012.

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Części książek na temat "Supervised neural networks"

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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.

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Melin, 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.

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Buscema, 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.

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Behnke, 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.

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Hvitfeldt, 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.

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Hvitfeldt, 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.

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Hammer, 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.

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Brabazon, 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.

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Fernández-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.

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Hajek, 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.

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Streszczenia konferencji na temat "Supervised neural networks"

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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.

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Berton, 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.

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Yin. "On asymptotic properties of supervised learning." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118540.

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Lamba, 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.

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Maggu, 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.

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Jordanov, 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.

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Wang, 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.

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Shukla, 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.

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Harrison, 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.

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Zhan, 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.

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Raporty organizacyjne na temat "Supervised neural networks"

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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.

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Farhi, 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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We introduce a quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning. The quantum circuit consists of a sequence of parameter dependent unitary transformations which acts on an input quantum state. For binary classification a single Pauli operator is measured on a designated readout qubit. The measured output is the quantum neural network’s predictor of the binary label of the input state. We show through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets. W
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