Готові списки джерел за темами / Unsupervised Neural Network

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Добірка наукової літератури з теми "Unsupervised Neural Network"

Автор: Grafiati
Опубліковано: 23 вересня 2022
Оновлено: 28 січня 2023

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Статті в журналах з теми "Unsupervised Neural Network"

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Banzi, Jamal, Isack Bulugu, and Zhongfu Ye. "Deep Predictive Neural Network: Unsupervised Learning for Hand Pose Estimation." International Journal of Machine Learning and Computing 9, no. 4 (August 2019): 432–39. http://dx.doi.org/10.18178/ijmlc.2019.9.4.822.

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2

Vamaraju, Janaki, and Mrinal K. Sen. "Unsupervised physics-based neural networks for seismic migration." Interpretation 7, no. 3 (August 1, 2019): SE189—SE200. http://dx.doi.org/10.1190/int-2018-0230.1.

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We have developed a novel framework for combining physics-based forward models and neural networks to advance seismic processing and inversion algorithms. Migration is an effective tool in seismic data processing and imaging. Over the years, the scope of these algorithms has broadened; today, migration is a central step in the seismic data processing workflow. However, no single migration technique is suitable for all kinds of data and all styles of acquisition. There is always a compromise on the accuracy, cost, and flexibility of these algorithms. On the other hand, machine-learning algorithms and artificial intelligence methods have been found immensely successful in applications in which big data are available. The applicability of these algorithms is being extensively investigated in scientific disciplines such as exploration geophysics with the goal of reducing exploration and development costs. In this context, we have used a special kind of unsupervised recurrent neural network and its variants, Hopfield neural networks and the Boltzmann machine, to solve the problems of Kirchhoff and reverse time migrations. We use the network to migrate seismic data in a least-squares sense using simulated annealing to globally optimize the cost function of the neural network. The weights and biases of the neural network are derived from the physics-based forward models that are used to generate seismic data. The optimal configuration of the neural network after training corresponds to the minimum energy of the network and thus gives the reflectivity solution of the migration problem. Using synthetic examples, we determine that (1) Hopfield neural networks are fast and efficient and (2) they provide reflectivity images with mitigated migration artifacts and improved spatial resolution. Specifically, the presented approach minimizes the artifacts that arise from limited aperture, low subsurface illumination, coarse sampling, and gaps in the data.
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3

Lin, Baihan. "Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers." Entropy 24, no. 1 (December 28, 2021): 59. http://dx.doi.org/10.3390/e24010059.

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Inspired by the adaptation phenomenon of neuronal firing, we propose the regularity normalization (RN) as an unsupervised attention mechanism (UAM) which computes the statistical regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle. Treating the neural network optimization process as a partially observable model selection problem, the regularity normalization constrains the implicit space by a normalization factor, the universal code length. We compute this universal code incrementally across neural network layers and demonstrate the flexibility to include data priors such as top-down attention and other oracle information. Empirically, our approach outperforms existing normalization methods in tackling limited, imbalanced and non-stationary input distribution in image classification, classic control, procedurally-generated reinforcement learning, generative modeling, handwriting generation and question answering tasks with various neural network architectures. Lastly, the unsupervised attention mechanisms is a useful probing tool for neural networks by tracking the dependency and critical learning stages across layers and recurrent time steps of deep networks.
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4

Jothilakshmi, S., V. Ramalingam, and S. Palanivel. "Unsupervised Speaker Segmentation using Autoassociative Neural Network." International Journal of Computer Applications 1, no. 7 (February 25, 2010): 24–30. http://dx.doi.org/10.5120/167-293.

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5

Zhang, Xiaowei, Jianming Lu, Nuo Zhang, and Takashi Yahagi. "Convolutive Nonlinear Separation with Unsupervised Neural Network." IEEJ Transactions on Electronics, Information and Systems 126, no. 8 (2006): 942–49. http://dx.doi.org/10.1541/ieejeiss.126.942.

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6

Intrator, Nathan. "Feature Extraction Using an Unsupervised Neural Network." Neural Computation 4, no. 1 (January 1992): 98–107. http://dx.doi.org/10.1162/neco.1992.4.1.98.

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A novel unsupervised neural network for dimensionality reduction that seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursuit methods is discussed. This leads to a new statistical insight into the synaptic modification equations governing learning in Bienenstock, Cooper, and Munro (BCM) neurons (1982). The importance of a dimensionality reduction principle based solely on distinguishing features is demonstrated using a phoneme recognition experiment. The extracted features are compared with features extracted using a backpropagation network.
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7

Pedrycz, W., and J. Waletzky. "Neural-network front ends in unsupervised learning." IEEE Transactions on Neural Networks 8, no. 2 (March 1997): 390–401. http://dx.doi.org/10.1109/72.557690.

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Dong-Chul Park. "Centroid neural network for unsupervised competitive learning." IEEE Transactions on Neural Networks 11, no. 2 (March 2000): 520–28. http://dx.doi.org/10.1109/72.839021.

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Ma, Chao, Yun Gu, Chen Gong, Jie Yang, and Deying Feng. "Unsupervised Video Hashing via Deep Neural Network." Neural Processing Letters 47, no. 3 (March 17, 2018): 877–90. http://dx.doi.org/10.1007/s11063-018-9812-x.

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Gunhan, Atilla E., László P. Csernai, and Jørgen Randrup. "UNSUPERVISED COMPETITIVE LEARNING IN NEURAL NETWORKS." International Journal of Neural Systems 01, no. 02 (January 1989): 177–86. http://dx.doi.org/10.1142/s0129065789000086.

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We study an idealized neural network that may approximate certain neurophysiological features of natural neural systems. The network contains a mutual lateral inhibition and is subjected to unsupervised learning by means of a Hebb-type learning principle. Its learning ability is analysed as a function of the strength of lateral inhibition and the training set.
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Дисертації з теми "Unsupervised Neural Network"

1

McConnell, Sabine. "An unsupervised neural network for the clustering of extragalactic objects." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2002. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ65638.pdf.

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ESTEU, BRUNO ROMANELLI MENECHINI. "CLUSTERING VIBRATION DATA FROM OIL WELLS THROUGH UNSUPERVISED NEURAL NETWORK." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25049@1.

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Анотація:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
A perfuração de poços de petróleo em águas profundas tem como objetivo atingir o melhor ponto de extração de óleo e gás natural presentes em reservatórios a alguns milhares de metros no fundo do mar. Um melhor entendimento da dinâmica de perfuração através da análise de parâmetros operacionais em tempo real é importante para otimizar os processos de perfuração e reduzir seus tempos de operação. Com esse objetivo, operadoras de petróleo têm realizado grandes investimentos no desenvolvimento de ferramentas de medição e transmissão de parâmetros durante a perfuração, tais como, entre outros, o peso sobre broca, rotação da coluna e vazão do fluido de perfuração. Dentre as vantagens em se monitorar estes dados em tempo real, destaca-se a otimização de parâmetros operacionais buscando obter uma taxa de penetração satisfatória com o menor gasto de energia possível. Em uma perfuração rotativa, essa energia é muitas vezes parcialmente dissipada devido à vibração da coluna causada pela interação entre broca e formação. Nesta dissertação, com o objetivo de extrair características comuns que pudessem vir a ajudar na otimização da atividade de perfuração, foi utilizada uma técnica de redes neurais não supervisionadas para análise de uma extensa base de dados levantados ao longo de campanhas de perfuração de poços em um mesmo campo de petróleo. Os dados de campo analisados foram obtidos ao longo de perfurações de poços verticais, exclusivamente empregando brocas tipo PDC e exibindo elevados níveis de vibração torcional. O estudo realizado a partir de registros de parâmetros de perfuração, características dos poços e respostas de vibração obtidas em tempo real por ferramentas de poço, e empregando o código de mineração de dados WEKA e a plataforma computacional de análise TIBCO Spotfire, permitiu a determinação de uma curva de desgaste de broca e a influência das ferramentas de navegação no nível de severidade de vibração ao longo da perfuração.
Drilling oil wells in deep waters aims to achieve the best point of extraction of oil and natural gas reservoirs present in a few thousand meters in the seabed. A better understanding of the drilling dynamics through the analysis of real time operation parameters is important to optimize drilling process and reduce operation time. For this purpose petroleum operator companies have been made great investments in developing tools that measure and transmit parameters during drilling operation, such as the weight on bit, pipes rotation per minute and drilling fluid flow. Among the advantages to monitor this real time data there is the operational parameters optimization looking for the least expenditure of energy as possible. In a rotary drilling operation this energy is often lost partially due to column vibration caused by the interaction between bit and formation.In this master s thesis in order to extract common features that could help on the drilling operation optimization a technique using unsupervised neural networks for analyze an extensive database which was built over drilling campaigns in a big oil field . The field data analyzed were obtained during drilling vertical wells exclusively employing PDC bits and presented high levels of torcional vibration. The study was made from drilling parameters records, wells characteristics and vibration responses obtained in real time by downhole tools. Employing the WEKA data mining code and the computing analysis platform TIBCO potfire it was possible determine a bit wear curve and the real influence of navigation tools on the severity levels of vibration during drilling operations.
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Mackenzie, Mathew David. "CDUL Class Directed Unsupervised Learning : an enhanced neural network classification system." Thesis, University of Kent, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360970.

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4

Huckle, Christopher Cedric. "Unsupervised categorization of word meanings using statistical and neural network methods." Thesis, University of Edinburgh, 1996. http://hdl.handle.net/1842/21308.

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A statistical technique is introduced for representing the contexts in which words occur. Each word is represented by a 'statistical context vector', and the vectors are subjected to hierarchical cluster analysis to produce a structure in which words which have similar contexts are placed closer together than those which do not. Analyses of this type are carried out on a 10,000,000 word corpus, using a variety of different parameters, and the appropriateness of the resulting structures is assessed using Roget's Thesaurus as a benchmark. A still more attractive approach is one which deals with polysemy, and which develops its representations for word meanings continuously from the outset, with no need for a separate stage of statistical analysis. To take these consideration into account, an unsupervised neural network is presented, in which different senses of a word token are assigned to different output clusters as the contexts of their occurrence dictate. After initial testing using Elman's (1988) artificial corpus, the network's performance is assessed on the 10,000,000 word corpus by comparing the ways in which different word tokens are distributed over the output units. Further analyses are carried out in which a crude measure of this distribution is assessed using Jones' (1985) 'Ease of Predication' measure. Ease of Predication is found to account for a significant amount of the variance in the distribution measure. Word frequency is also found to play a significant role, and word frequency effects are reassessed in the light of this. The psychological implications of the results obtained from the network are discussed. It is concluded that there is a great deal of information inherent in the structure of language which could potentially play an important part in developing a conceptual structure for word meanings. Whilst extralinguistic information is undoubtedly likely to be of importance as well, it is striking that the use of very simple statistical measures can permit the development of such rich structures.
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5

Srinivasan, BadriNarayanan. "Unsupervised learning to cluster the disease stages in parkinson's disease." Thesis, Högskolan Dalarna, Datateknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:du-5499.

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Parkinson's disease (PD) is the second most common neurodegenerative disorder (after Alzheimer's disease) and directly affects upto 5 million people worldwide. The stages (Hoehn and Yaar) of disease has been predicted by many methods which will be helpful for the doctors to give the dosage according to it. So these methods were brought up based on the data set which includes about seventy patients at nine clinics in Sweden. The purpose of the work is to analyze unsupervised technique with supervised neural network techniques in order to make sure the collected data sets are reliable to make decisions. The data which is available was preprocessed before calculating the features of it. One of the complex and efficient feature called wavelets has been calculated to present the data set to the network. The dimension of the final feature set has been reduced using principle component analysis. For unsupervised learning k-means gives the closer result around 76% while comparing with supervised techniques. Back propagation and J4 has been used as supervised model to classify the stages of Parkinson's disease where back propagation gives the variance percentage of 76-82%. The results of both these models have been analyzed. This proves that the data which are collected are reliable to predict the disease stages in Parkinson's disease.
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6

Sani, Lorenzo. "Unsupervised clustering of MDS data using federated learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25591/.

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In this master thesis we developed a model for unsupervised clustering on a data set of biomedical data. This data has been collected by GenoMed4All consortium from patients affected by Myelodysplastic Syndrome (MDS), that is an haematological disease. The main focus is put on the genetic mutations collected that are used as features of the patients in order to cluster them. Clustering approaches have been used in several studies concerning haematological diseases such MDS. A neural network-based model was used to solve the task. The results of the clustering have been compared with labels from a "gold standard'' technique, i.e. hierarchical Dirichlet processes (HDP). Our model was designed to be also implemented in the context of federated learning (FL). This innovative technique is able to achieve machine learning objective without the necessity of collecting all the data in one single center, allowing strict privacy policies to be respected. Federated learning was used because of its properties, and because of the sensitivity of data. Several recent studies regarding clinical problems addressed with machine learning endorse the development of federated learning settings in such context, because its privacy preserving properties could represent a cornerstone for applying machine learning techniques to medical data. In this work will be then discussed the clustering performance of the model, and also its generative capabilities.
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7

Mici, Luiza [Verfasser], and Stefan [Akademischer Betreuer] Wermter. "Unsupervised Learning of Human-Object Interactions with Neural Network Self-Organization / Luiza Mici ; Betreuer: Stefan Wermter." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2018. http://d-nb.info/117430653X/34.

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8

Di, Felice Marco. "Unsupervised anomaly detection in HPC systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Alla base di questo studio vi è l'analisi di tecniche non supervisionate applicate per il rilevamento di stati anomali in sistemi HPC, complessi calcolatori capaci di raggiungere prestazioni dell'ordine dei PetaFLOPS. Nel mondo HPC, per anomalia si intende un particolare stato che induce un cambiamento delle prestazioni rispetto al normale funzionamento del sistema. Le anomalie possono essere di natura diversa come il guasto che può riguardare un componente, una configurazione errata o un'applicazione che entra in uno stato inatteso provocando una prematura interruzione dei processi. I datasets utilizzati in un questo progetto sono stati raccolti da D.A.V.I.D.E., un reale sistema HPC situato presso il CINECA di Casalecchio di Reno, o sono stati generati simulando lo stato di un singolo nodo di un virtuale sistema HPC analogo a quello del CINECA modellato secondo specifiche funzioni non lineari ma privo di rumore. Questo studio propone un approccio inedito, quello non supervisionato, mai applicato prima per svolgere anomaly detection in sistemi HPC. Si è focalizzato sull'individuazione dei possibili vantaggi indotti dall'uso di queste tecniche applicate in tale campo. Sono stati realizzati e mostrati alcuni casi che hanno prodotto raggruppamenti interessanti attraverso le combinazioni di Variational Autoencoders, un particolare tipo di autoencoder probabilistico con la capacità di preservare la varianza dell'input set nel suo spazio latente, e di algoritmi di clustering, come K-Means, DBSCAN, Gaussian Mixture ed altri già noti in letteratura.
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9

Ackerman, Wesley. "Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8684.

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We expand the scope of image-to-image translation to include more distinct image domains, where the image sets have analogous structures, but may not share object types between them. Semantic-Driven Unsupervised Image-to-Image Translation for Distinct Image Domains (SUNIT) is built to more successfully translate images in this setting, where content from one domain is not found in the other. Our method trains an image translation model by learning encodings for semantic segmentations of images. These segmentations are translated between image domains to learn meaningful mappings between the structures in the two domains. The translated segmentations are then used as the basis for image generation. Beginning image generation with encoded segmentation information helps maintain the original structure of the image. We qualitatively and quantitatively show that SUNIT improves image translation outcomes, especially for image translation tasks where the image domains are very distinct.
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Lin, Brian K. "An unsupervised neural network fault discriminating system implementation for on-line condition monitoring and diagnostics of induction machines." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/14957.

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Книги з теми "Unsupervised Neural Network"

1

Baruque, Bruno. Fusion methods for unsupervised learning ensembles. Berlin: Springer, 2010.

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2

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

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3

Whitehead, P. A. Design considerations for a hardware accelerator for Kohonen unsupervised learning in artificial neural networks. Manchester: UMIST, 1997.

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4

Szu, Harold H., and Jack Agee. Independent component analyses, wavelets, unsupervised nano-biomimetic sensors, and neural networks VI: 17-19 March 2008, Orlando, Florida, USA. Bellingham, Wash: SPIE, 2008.

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5

Campin, Michael James. Sigma-Delta modulator fault diagnosis using an unsupervised expert network. 1992.

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6

E, Hinton Geoffrey, and Sejnowski Terrence J, eds. Unsupervised learning: Foundations of neural computation. Cambridge, Mass: MIT Press, 1999.

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7

Sejnowski, Terrence J., and Geoffrey Hinton. Unsupervised Learning: Foundations of Neural Computation. MIT Press, 1999.

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8

Sejnowski, Terrence J., Tomaso A. Poggio, and Geoffrey Hinton. Unsupervised Learning: Foundations of Neural Computation. MIT Press, 2016.

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9

Baruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2010.

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10

Baruque, Bruno. Fusion Methods for Unsupervised Learning Ensembles. Springer, 2014.

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Частини книг з теми "Unsupervised Neural Network"

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Vermeulen, Andreas François. "Unsupervised Learning: Neural Network Toolkits." In Industrial Machine Learning, 207–23. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5316-8_7.

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2

Baldi, Pierre, Yves Chauvin, and Kurt Hornik. "Supervised and Unsupervised Learning in Linear Networks." In International Neural Network Conference, 825–28. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_99.

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3

Niros, Antonios D., and George E. Tsekouras. "A Radial Basis Function Neural Network Training Mechanism for Pattern Classification Tasks." In Unsupervised Learning Algorithms, 193–206. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_8.

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4

Furao, Shen, and Osamu Hasegawa. "An Incremental Neural Network for Non-stationary Unsupervised Learning." In Neural Information Processing, 641–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_98.

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Fletcher, Peter. "A spontaneously growing network for unsupervised learning." In Theory and Applications of Neural Networks, 149–63. London: Springer London, 1992. http://dx.doi.org/10.1007/978-1-4471-1833-6_9.

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6

Acciani, Giuseppe, Ernesto Chiarantoni, Daniela Girimonte, and Cataldo Guaragnella. "Unsupervised - Neural Network Approach for Efficient Video Description." In Artificial Neural Networks — ICANN 2002, 1305–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_211.

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7

Zhao, Ye, Xiaobin Hu, Xueliang Liu, and Chunxiao Fan. "Learning Unsupervised Video Summarization with Semantic-Consistent Network." In Neural Computing for Advanced Applications, 207–19. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7670-6_18.

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8

Chiarantoni, Ernesto, Giuseppe Acciani, Girolamo Fornarelli, and Silvano Vergura. "Robust Unsupervised Competitive Neural Network by Local Competitive Signals." In Artificial Neural Networks — ICANN 2002, 963–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_156.

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9

Liang, Yu, Yi Yang, Furao Shen, Jinxi Zhao, and Tao Zhu. "An Incremental Deep Learning Network for On-line Unsupervised Feature Extraction." In Neural Information Processing, 383–92. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70096-0_40.

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Oberhoff, Daniel, and Marina Kolesnik. "Unsupervised Bayesian Network Learning for Object Recognition in Image Sequences." In Artificial Neural Networks - ICANN 2008, 235–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87536-9_25.

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Тези доповідей конференцій з теми "Unsupervised Neural Network"

1

Bui, The Duy, Duy Khuong Nguyen, and Tien Dat Ngo. "Supervising an Unsupervised Neural Network." In 2009 First Asian Conference on Intelligent Information and Database Systems, ACIIDS. IEEE, 2009. http://dx.doi.org/10.1109/aciids.2009.92.

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Kim, Yoon, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, and Gábor Melis. "Unsupervised Recurrent Neural Network Grammars." In Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-1114.

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3

Ghahramani, Z. "Scaling in a hierarchical unsupervised network." In 9th International Conference on Artificial Neural Networks: ICANN '99. IEE, 1999. http://dx.doi.org/10.1049/cp:19991077.

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Li, C. James, and C. Jansuwan. "Projection Network for Unsupervised Pattern Classification." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79603.

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Анотація:
Projection network, being a non-linear dynamic system itself, has been shown to be superior to static classifiers such as neural networks in some applications where noise is significant. However it is a supervised classifier by nature. To extend its utility for unsupervised classification, this study proposes an unsupervised pattern classifier integrating a clustering algorithm based on DBSCAN and a dynamic classifier based on the projection network. The former is used to form clusters out of un-labeled data and eliminate outliers. Then, significant clusters in terms of size are identified. Subsequently, a system of projection networks is established to recognize all the significant clusters. The unsupervised classifier is tested with three well-known benchmark data sets (by ignoring data labels during training) including the Fisher’s iris data, the heart disease data and the credit screening data and the results are compared to those of supervised classifiers based on the projection network. The difference in performance is small. However, the ability of unsupervised classification comes at a price of a more complex classifier system and the need of data pre-conditioning. The former is because more than one cluster could be formed for a class and therefore more computational units are needed for the classifier, and the latter is because increased similarity of data after clustering increases the chances of numerical instability in the least square algorithm used to initialize the classifier.
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5

Zhu, Junyou, Zheng Luo, Fan Zhang, Haiqiang Wang, Jiaxin Wang, and Chao Gao. "Unsupervised Dynamic Network Embedding Using Global Information." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533668.

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6

Cai, Dejun, Wei Wang, and Faguang Wan. "Unsupervised neural network algorithm for image compression." In San Diego '92, edited by Su-Shing Chen. SPIE, 1992. http://dx.doi.org/10.1117/12.130879.

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Liu, Lurng-Kuo, and Panos A. Ligomenides. "Unsupervised orthogonalization neural network for image compression." In Applications in Optical Science and Engineering, edited by David P. Casasent. SPIE, 1992. http://dx.doi.org/10.1117/12.131602.

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Wang, Yifan, Hisao Ishibuchi, Jihua Zhu, Yaxiong Wang, and Tao Dai. "Unsupervised Fuzzy Neural Network for Image Clustering." In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021. http://dx.doi.org/10.1109/fuzz45933.2021.9494601.

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9

Li, Xingjian, Ping Su, and Bizhong Xia. "Lensless magnified holographic projection based on an unsupervised neural network technology." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.w5a.29.

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A new method is proposed to generate amplitude-only holograms in a lensless magnified projection system based on the unsupervised neural network technology. Simulation demonstrated that the method can generate high-quality holograms in 0.22s.
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Meyer-Baese, A., V. Thummler, and F. Theis. "Stability Analysis of an Unsupervised Competitive Neural Network." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246799.

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Звіти організацій з теми "Unsupervised Neural Network"

1

Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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Анотація:
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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2

Chavez, Wesley. An Exploration of Linear Classifiers for Unsupervised Spiking Neural Networks with Event-Driven Data. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6323.

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