Relevant bibliographies by topics / Supervised neural networks

Academic literature on the topic 'Supervised neural networks'

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Published: 4 June 2021
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Journal articles on the topic "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 based on different types of recurrent cells and discusses their suitability for such analysis.
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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 iteratively using the derived self organizing backpropagation algorithm. The ability of the self-organizing network to generalize on unseen data is also experimentally evaluated on real data sets and compares favorably with the traditional labeled supervision with neural networks. In addition, interesting features emerge out of the proposed self-organizing supervision, which are absent in conventional approaches.
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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 been successfully tested in the presence of noise, requires smaller networks than reservoir computing, and results in faster convergence than existing algorithms for similar tasks such as SpikeProp.
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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 preprocessing algorithm based on normalized sample symmetry of bearing is adopted to greatly increase the number of samples. Two different convolutional neural networks, supervised networks and unsupervised networks, are tested separately for the bearing defect detection. The first experiment adopts the supervised networks, and ResNet neural networks are selected as the supervised networks in this experiment. The experiment result shows that the AUC of the model is 0.8567, which is low for the actual use. Also, the positive and negative samples should be labelled manually. To improve the AUC of the model and the flexibility of the samples labelling, a new unsupervised neural network based on autoencoder networks is proposed. Gradients of the unlabeled data are used as labels, and autoencoder networks are created with U-net to predict the output. In the second experiment, positive samples of the supervised experiment are used as the training set. The experiment of the unsupervised neural networks shows that the AUC of the model is 0.9721. In this experiment, the AUC is higher than the first experiment, but the positive samples must be selected. To overcome this shortage, the dataset of the third experiment is the same as the supervised experiment, where all the positive and negative samples are mixed together, which means that there is no need to label the samples. This experiment shows that the AUC of the model is 0.9623. Although the AUC is slightly lower than that of the second experiment, the AUC is high enough for actual use. The experiment results demonstrate the feasibility and superiority of the proposed unsupervised networks.
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Dissertations / Theses on the topic "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 demonstrations show that these types of neural networks are computationally more powerful than net- works of rate neurons. Conversely, the existing learning algorithms no longer apply to spik- ing neural networks. Supervised learning algorithms based on gradient descent, such as SpikeProp and its extensions, have been developed for spiking neural networks with multiple layers, but these ate limited to a specific model of neurons, with only the first spike being consid- ered. Another learning algorithm, ReSuMe, for single layer networks is based on spike-timing dependent plasticity ~STDP) and uses the computational power of multiple spikes; moreover, this algorithm is not limited to a specific neuron model. The algorithm presented here is based on the gradient descent method, while making use of STDP and can be applied to networks with multi- ple layers. Furthermore, the algorithm is not limited to neurons firing single spikes or specific neuron models. Results on classic benchmarks, such as the XOR problem and the Iris data set, show that the algo- rithm is capable of non-linear transformations. Complex classification tasks have also been applied with fast convergence times. The results of the simulations show that the new learning rule is as efficient as SpikeProp while having all the advantages of STDP. The supervised learning algorithm for spiking neurons is compared with the back- propagation algorithm for rate neurons by modelling an audio-visual perceptual illusion, the McGurk effect.
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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 continuity to the projection using two ELMs. This new methodology shows considerable improvement over SOM on real datasets. Second, as a Supervised Learning method, ELMs has been applied to the hierarchical multiscale method to bridge the the molecular dynamics to continua. The method is tested on simulation data and proven to be efficient for passing the information from one scale to another. Lastly, the regularization of ELMs has been studied and a new regularization algorithm for ELMs is created using a modified Lanczos Algorithm. The Lanczos ELM on average divide computational time by 20 and reduce the Normalized MSE by 14% comparing with regular ELMs.
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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 the learning problem to the establishment of the corresponding optimization problem. A novel strategy for the systematic and automatic design of RBF networks is developed based upon the coordinated evaluation of memorization and generalization of an incremental architecture. The architecture grows according to the monotonous increase of its generalization. Its corresponding learning method stands out due to its fast convergence and robustness. It represents one of the few learning methods whose computational complexity is precisely stated. It can be used in any non-linear regression tasks which are common in different disciplines of the natural and engineering sciences. Four learning methods are implemented for evaluation. The most complex is the one for the novel self-generating network architecture. Another learning method constitutes a strong contribution to the area of robust learning allowing the automatic detection of data outliers and the removal of their negative influence in the network approximation. It represents the first robust learning method for RBF networks available in the literature and is integrated into the overall strategy introduced in this work. Diverse functions are used to simulate training and test data. Data generated for evaluation is: noise-free, noisy, and with outliers as well as one- and multidimensional. The data with outliers allows the verification of the robustness of the introduced method. In addition, an evaluation example from the area of sensory data processing is chosen. This example consists in localizing a generic object based on range information in the framework of a grasping strategy. The relation to other works and a perspective for further research concludes this work.
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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 thesis deals with the description of the creation of code for working with datasets and the application of the SSL methods Rotation, SimCLR, MoCo and BYOL in the role of classification and semantic segmentation. Each application of the method is explained in detail and evaluated for various parameters on the large STL10 dataset. Subsequently, the success of the methods is evaluated for different datasets and the limiting conditions in the classification task are named. The practical part concludes with the application of SSL methods for pre-training the encoder in the application of semantic segmentation with the Cityscapes dataset.
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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 coincidence detectors. Previous temporal coding spiking neural networks, realising spiking neurons as integrators, were utilised for analytical studies. Temporal coding spiking neural networks applied successfully for clustering and classification tasks realised spiking neurons as coincidence detectors and encoded input in formation in the connection delays through a weight adaptation technique. These learning models select suitably delayed connections by enhancing the weights of those connections while weakening the others. This research investigates the learning in temporal coding spiking neural networks with spiking neurons as integrators and coincidence detectors. Focus is given to both supervised and unsupervised learning through weight as well as through delay adaptation. Three novel models for learning in temporal coding spiking neural networks are presented in this research. The first spiking neural network model, Self- Organising Weight Adaptation Spiking Neural Network (SOWA_SNN) realises the spiking neuron as integrator. This model adapts and encodes input information in its connection weights. The second learning model, Self-Organising Delay Adaptation Spiking Neural Network (SODA_SNN) and the third model, Super vised Delay Adaptation Spiking Neural Network (SDA_SNN) realise the spiking neuron as coincidence detector. These two models adapt the connection delays in order to detect temporal patterns through coincidence detection. The first two models were developed for clustering applications and the third for classification tasks. All three models employ Hebbian-based learning rules to update the network connection parameters by utilising the difference between the input and output spike times. The proposed temporal coding spiking neural network models were implemented as discrete models in software and their characteristics and capabilities were analysed through simulations on three bench mark data sets and a high dimensional data set. All three models were able to cluster or classify the analysed data sets efficiently with a high degree of accuracy. The performance of the proposed models, was found to be better than the existing spiking neural network models as well as conventional neural networks. The proposed learning paradigms could be applied to a wide range of applications including manufacturing, business and biomedical domains.
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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 spécifique à la classe. Tout d’abord, un ensemble de candidats est calculé, ceux-ci représentant les positions possibles de l’objet pour le filtre racine initial, en se basant sur une mesure générique d’objectness (par region proposals) pour combiner les régions les plus saillantes et potentiellement de bonne qualité. Ensuite, nous avons proposé l’apprentissage du label des classes latentes de chaque candidat comme un problème de classification binaire, en entrainant des classifieurs spécifiques pour chaque catégorie afin de prédire si les candidats sont potentiellement des objets cible ou non. De plus, nous avons amélioré la détection en incorporant l’information contextuelle à partir des scores de classification de l’image. Enfin, nous avons élaboré une procédure de post-traitement permettant d’élargir et de contracter les régions fournies par le DPM afin de les adapter efficacement à la taille de l’objet, augmentant ainsi la précision finale de la détection. Pour la seconde approche, nous avons étudié dans quelle mesure l’information tirée des objets similaires d’un point de vue visuel et sémantique pouvait être utilisée pour transformer un classifieur d’images en détecteur d’objets d’une manière semi-supervisée sur un large ensemble de données, pour lequel seul un sous-ensemble des catégories d’objets est annoté avec des boîtes englobantes nécessaires pour l’apprentissage des détecteurs. Nous avons proposé de transformer des classifieurs d’images basés sur des réseaux convolutionnels profonds (Deep CNN) en détecteurs d’objets en modélisant les différences entre les deux en considérant des catégories disposant à la fois de l’annotation au niveau de l’image globale et l’annotation au niveau des boîtes englobantes. Cette information de différence est ensuite transférée aux catégories sans annotation au niveau des boîtes englobantes, permettant ainsi la conversion de classifieurs d’images en détecteurs d’objets. Nos approches ont été évaluées sur plusieurs jeux de données tels que PASCAL VOC, ImageNet ILSVRC et Microsoft COCO. Ces expérimentations ont démontré que nos approches permettent d’obtenir des résultats comparables à ceux de l’état de l’art et qu’une amélioration significative a pu être obtenue par rapport à des méthodes récentes de détection d’objets faiblement supervisées
In 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
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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 productivity and customer satisfaction of using machine learning to sort customer needs? The data was collected from the internal server database of a large digital company and was then trained on and tested with the three classification algorithms. Furthermore, a survey was collected with questions focused on understanding how the current system affects the involved employees. A first finding indicates that neural networks is the best suited model for the classification task. Though, when the scope and complexity was limited, naïve Bayes and logistic regression performed sufficiently. A second finding of the study is that the classification model potentially improves productivity given that the baseline is met. However, a difficulty exists in drawing conclusions on the exact effects on customer satisfaction since there are many aspects to take into account. Nevertheless, there is a good potential to achieve a positive net effect.
Syftet 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.
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Books on the topic "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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Book chapters on the topic "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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Conference papers on the topic "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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Reports on the topic "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. We then discuss presenting the data as quantum superpositions of computational basis states corresponding to different label values. Here we show through simulation that learning is possible. We consider using our QNN to learn the label of a general quantum state. By example we show that this can be done. Our work is exploratory and relies on the classical simulation of small quantum systems. The QNN proposed here was designed with near-term quantum processors in mind. Therefore it will be possible to run this QNN on a near term gate model quantum computer where its power can be explored beyond what can be explored with simulation.
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