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Статті в журналах з теми "Semi- and unsupervised learning"
Gao Huang, Shiji Song, Jatinder N. D. Gupta, and Cheng Wu. "Semi-Supervised and Unsupervised Extreme Learning Machines." IEEE Transactions on Cybernetics 44, no. 12 (December 2014): 2405–17. http://dx.doi.org/10.1109/tcyb.2014.2307349.
Повний текст джерелаC A Padmanabha Reddy, Y., P. Viswanath, and B. Eswara Reddy. "Semi-supervised learning: a brief review." International Journal of Engineering & Technology 7, no. 1.8 (February 9, 2018): 81. http://dx.doi.org/10.14419/ijet.v7i1.8.9977.
Повний текст джерелаHui, Binyuan, Pengfei Zhu, and Qinghua Hu. "Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4215–22. http://dx.doi.org/10.1609/aaai.v34i04.5843.
Повний текст джерелаGuo, Wenbin, and Juan Zhang. "Semi-supervised learning for raindrop removal on a single image." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 4041–49. http://dx.doi.org/10.3233/jifs-212342.
Повний текст джерелаNiu, Gang, Bo Dai, Makoto Yamada, and Masashi Sugiyama. "Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization." Neural Computation 26, no. 8 (August 2014): 1717–62. http://dx.doi.org/10.1162/neco_a_00614.
Повний текст джерелаZhang, Ziji, Peng Zhang, Peineng Wang, Jawaad Sheriff, Danny Bluestein, and Yuefan Deng. "Rapid analysis of streaming platelet images by semi-unsupervised learning." Computerized Medical Imaging and Graphics 89 (April 2021): 101895. http://dx.doi.org/10.1016/j.compmedimag.2021.101895.
Повний текст джерелаAkdemir, Deniz, and Jean-Luc Jannink. "Ensemble learning with trees and rules: Supervised, semi-supervised, unsupervised." Intelligent Data Analysis 18, no. 5 (July 16, 2014): 857–72. http://dx.doi.org/10.3233/ida-140672.
Повний текст джерелаShutova, Ekaterina, Lin Sun, Elkin Darío Gutiérrez, Patricia Lichtenstein, and Srini Narayanan. "Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning." Computational Linguistics 43, no. 1 (April 2017): 71–123. http://dx.doi.org/10.1162/coli_a_00275.
Повний текст джерелаWeinlichová, Jana, and Jiří Fejfar. "Usage of self-organizing neural networks in evaluation of consumer behaviour." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no. 6 (2010): 625–32. http://dx.doi.org/10.11118/actaun201058060625.
Повний текст джерелаYamkovyi, Klym. "DEVELOPMENT AND COMPARATIVE ANALYSIS OF SEMI-SUPERVISED LEARNING ALGORITHMS ON A SMALL AMOUNT OF LABELED DATA." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (5) (July 12, 2021): 98–103. http://dx.doi.org/10.20998/2079-0023.2021.01.16.
Повний текст джерелаДисертації з теми "Semi- and unsupervised learning"
Zhang, Pin. "Nonlinear Semi-supervised and Unsupervised Metric Learning with Applications in Neuroimaging." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1525266545968548.
Повний текст джерелаKilinc, Ismail Ozsel. "Graph-based Latent Embedding, Annotation and Representation Learning in Neural Networks for Semi-supervised and Unsupervised Settings." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7415.
Повний текст джерелаTrivedi, Shubhendu. "A Graph Theoretic Clustering Algorithm based on the Regularity Lemma and Strategies to Exploit Clustering for Prediction." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/573.
Повний текст джерелаOLIVEIRA, Paulo César de. "Abordagem semi-supervisionada para detecção de módulos de software defeituosos." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/19990.
Повний текст джерелаMade available in DSpace on 2017-07-24T12:11:04Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) Dissertação Mestrado Paulo César de Oliveira.pdf: 2358509 bytes, checksum: 36436ca63e0a8098c05718bbee92d36e (MD5) Previous issue date: 2015-08-31
Com a competitividade cada vez maior do mercado, aplicações de alto nível de qualidade são exigidas para a automação de um serviço. Para garantir qualidade de um software, testá-lo visando encontrar falhas antecipadamente é essencial no ciclo de vida de desenvolvimento. O objetivo do teste de software é encontrar falhas que poderão ser corrigidas e consequentemente, aumentar a qualidade do software em desenvolvimento. À medida que o software cresce, uma quantidade maior de testes é necessária para prevenir ou encontrar defeitos, visando o aumento da qualidade. Porém, quanto mais testes são criados e executados, mais recursos humanos e de infraestrutura são necessários. Além disso, o tempo para realizar as atividades de teste geralmente não é suficiente, fazendo com que os defeitos possam escapar. Cada vez mais as empresas buscam maneiras mais baratas e efetivas para detectar defeitos em software. Muitos pesquisadores têm buscado nos últimos anos, mecanismos para prever automaticamente defeitos em software. Técnicas de aprendizagem de máquina vêm sendo alvo das pesquisas, como uma forma de encontrar defeitos em módulos de software. Tem-se utilizado muitas abordagens supervisionadas para este fim, porém, rotular módulos de software como defeituosos ou não para fins de treinamento de um classificador é uma atividade muito custosa e que pode inviabilizar a utilização de aprendizagem de máquina. Neste contexto, este trabalho propõe analisar e comparar abordagens não supervisionadas e semisupervisionadas para detectar módulos de software defeituosos. Para isto, foram utilizados métodos não supervisionados (de detecção de anomalias) e também métodos semi-supervisionados, tendo como base os classificadores AutoMLP e Naive Bayes. Para avaliar e comparar tais métodos, foram utilizadas bases de dados da NASA disponíveis no PROMISE Software Engineering Repository.
Because the increase of market competition then high level of quality applications are required to provide automate services. In order to achieve software quality testing is essential in the development lifecycle with the purpose of finding defect as earlier as possible. The testing purpose is not only to find failures that can be fixed, but improve software correctness and quality. Once software gets more complex, a greater number of tests will be necessary to prevent or find defects. Therefore, the more tests are designed and exercised, the more human and infrastructure resources are needed. However, time to run the testing activities are not enough, thus, as a result, it causes escape defects. Companies are constantly trying to find cheaper and effective ways to software defect detection in earlier stages. In the past years, many researchers are trying to finding mechanisms to automatically predict these software defects. Machine learning techniques are being a research target, as a way of finding software modules detection. Many supervised approaches are being used with this purpose, but labeling software modules as defective or not defective to be used in training phase is very expensive and it can make difficult machine learning use. Considering that this work aims to analyze and compare unsupervised and semi-supervised approaches to software module defect detection. To do so, unsupervised methods (of anomaly detection) and semi-supervised methods using AutoMLP and Naive Bayes algorithms were used. To evaluate and compare these approaches, NASA datasets were used at PROMISE Software Engineering Repository.
Packer, Thomas L. "Scalable Detection and Extraction of Data in Lists in OCRed Text for Ontology Population Using Semi-Supervised and Unsupervised Active Wrapper Induction." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4258.
Повний текст джерелаChoi, Jin-Woo. "Action Recognition with Knowledge Transfer." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/101780.
Повний текст джерелаDoctor of Philosophy
Recent progress on deep learning has shown remarkable action recognition performance. The remarkable performance is often achieved by transferring the knowledge learned from existing large-scale data to the small-scale data specific to applications. However, existing action recog- nition models do not always work well on new tasks and datasets because of the following two problems. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor performance on the new datasets and tasks. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and target distributions are different. Fine-tuning the model on the target data can mitigate this issue. However, manual labeling small-scale target videos is labor-intensive. In this dissertation, I propose solutions to these two problems. To tackle the first problem, I propose to learn scene-invariant action representations to mitigate background scene- biased human action recognition models for the first problem. Specifically, the proposed method learns representations that cannot predict the scene types and the correct actions when there is no evidence. I validate the proposed method's effectiveness by transferring the pre-trained model to multiple action understanding tasks. The results show consistent improvement over the baselines for every task and dataset. To handle the second problem, I formulate human action recognition as an unsupervised learning problem on the target data. In this setting, we have many labeled videos as source data and unlabeled videos as target data. We can use already existing labeled video datasets as source data in this setting. The task is to align the source and target feature distributions so that the learned model can generalize well on the target data. I propose 1) aligning the more important temporal part of each video and 2) encouraging the model to focus on action, not the background scene. The proposed method is simple and intuitive while achieving state-of-the-art performance without training on a lot of labeled target videos. I relax the unsupervised target data setting to a sparsely labeled target data setting. Here, we have many labeled videos as source data and sparsely labeled videos as target data. The setting is practical as sometimes we can afford a little bit of cost for labeling target data. I propose multiple video data augmentation methods to inject color, spatial, temporal, and scene invariances to the action recognition model in this setting. The resulting method shows favorable performance on the public benchmarks.
Verri, Filipe Alves Neto. "Collective dynamics in complex networks for machine learning." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-18102018-113054/.
Повний текст джерелаAprendizado de máquina permite que computadores aprendam automaticamente dos dados. Na literatura, métodos baseados em grafos recebem crescente atenção por serem capazes de aprender através de informações locais e globais. Nestes métodos, cada item de dado é um vértice e as conexões são dadas uma regra de afinidade. Todavia, tais técnicas possuem custo de tempo impraticável para grandes grafos. O uso de heurísticas supera este problema, encontrando soluções subótimas em tempo factível. No início, alguns métodos de otimização inspiraram suas heurísticas em processos naturais coletivos, como formigas procurando por comida e enxames de abelhas. Atualmente, os avanços na área de sistemas complexos provêm ferramentas para medir e entender estes sistemas. Redes complexas, as quais são grafos com topologia não trivial, são uma das ferramentas. Elas são capazes de descrever as relações entre topologia, estrutura e dinâmica de sistemas complexos. Deste modo, novos métodos de aprendizado baseados em redes complexas e dinâmica coletiva vêm surgindo. Eles atuam em três passos. Primeiro, uma rede complexa é construída da entrada. Então, simula-se um sistema coletivo distribuído na rede para obter informações. Enfim, a informação coletada é utilizada para resolver o problema. A interação entre indivíduos no sistema permite alcançar uma dinâmica muito mais complexa do que o comportamento individual. Nesta pesquisa, estudei o uso de dinâmica coletiva em problemas de aprendizado de máquina, tanto em casos não supervisionados como semissupervisionados. Especificamente, propus um novo sistema de competição de partículas cuja competição ocorre em arestas ao invés de vértices, aumentando a informação do sistema. Ainda, o sistema proposto é o primeiro modelo de competição de partículas aplicado em aprendizado de máquina com comportamento determinístico. Resultados comprovam várias vantagens do modelo em arestas, includindo detecção de áreas sobrepostas, melhor exploração do espaço e convergência mais rápida. Além disso, apresento uma nova técnica de formação de redes que não é baseada na similaridade dos dados e possui baixa complexidade computational. Uma vez que o custo de inserção e remoção de exemplos na rede é barato, o método pode ser aplicado em aplicações de tempo real. Finalmente, conduzi um estudo analítico em um sistema de alinhamento de partículas. O estudo foi necessário para garantir o comportamento esperado na aplicação do sistema em problemas de detecção de comunidades. Em suma, os resultados da pesquisa contribuíram para várias áreas de aprendizado de máquina e sistemas complexos.
Bernardini, Alessandra. "Extraction of Grasping Motions from sEMG Signals for the Control of Robotic Hands through Autoencoding." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
Знайти повний текст джерелаKahindo, Senge Muvingi Christian. "Analyse automatique de l’écriture manuscrite sur tablette pour la détection et le suivi thérapeutique de personnes présentant des pathologies." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL016/document.
Повний текст джерелаWe present, in this thesis, a novel paradigm for assessing Alzheimer’s disease by analyzing impairment of handwriting (HW) on tablets, a challenging problem that is still in its infancy. The state of the art is dominated by methods that assume a unique behavioral trend for each cognitive profile, and that extract global kinematic parameters, assessed by standard statistical tests or classification models, for discriminating the neuropathological disorders (Alzheimer’s (AD), Mild Cognitive Impairment (MCI)) from Healthy Controls (HC). Our work tackles these two major limitations as follows. First, instead of considering a unique behavioral pattern for each cognitive profile, we relax this heavy constraint by allowing the emergence of multimodal behavioral patterns. We achieve this by performing semi-supervised learning to uncover homogeneous clusters of subjects, and then we analyze how much information these clusters carry on the cognitive profiles. Second, instead of relying on global kinematic parameters, mostly consisting of their average, we refine the encoding either by a semi-global parameterization, or by modeling the full dynamics of each parameter, harnessing thereby the rich temporal information inherently characterizing online HW. Thanks to our modeling, we obtain new findings that are the first of their kind on this research field. A striking finding is revealed: two major clusters are unveiled, one dominated by HC and MCI subjects, and one by MCI and ES-AD, thus revealing that MCI patients have fine motor skills leaning towards either HC’s or ES-AD’s. This thesis introduces also a new finding from HW trajectories that uncovers a rich set of features simultaneously like the full velocity profile, size and slant, fluidity, and shakiness, and reveals, in a naturally explainable way, how these HW features conjointly characterize, with fine and subtle details, the cognitive profiles
Cupertino, Thiago Henrique. "Machine learning via dynamical processes on complex networks." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25032014-154520/.
Повний текст джерелаA extração de conhecimento útil a partir de conjuntos de dados é um conceito chave em sistemas de informação modernos. Por conseguinte, a necessidade de técnicas eficientes para extrair o conhecimento desejado vem crescendo ao longo do tempo. Aprendizado de máquina é uma área de pesquisa dedicada ao desenvolvimento de técnicas capazes de permitir que uma máquina \"aprenda\" a partir de conjuntos de dados. Muitas técnicas já foram propostas, mas ainda há questões a serem reveladas especialmente em pesquisas interdisciplinares. Nesta tese, exploramos as vantagens da representação de dados em rede para desenvolver técnicas de aprendizado de máquina baseadas em processos dinâmicos em redes. A representação em rede unifica a estrutura, a dinâmica e as funções do sistema representado e, portanto, é capaz de capturar as relações espaciais, topológicas e funcionais dos conjuntos de dados sob análise. Desenvolvemos técnicas baseadas em rede para os três paradigmas de aprendizado de máquina: supervisionado, semissupervisionado e não supervisionado. O processo dinâmico de passeio aleatório é utilizado para caracterizar o acesso de dados não rotulados às classes de dados configurando uma nova heurística no paradigma supervisionado, a qual chamamos de facilidade de acesso. Também propomos uma técnica de classificação de dados que combina a visão de alto nível dos dados, por meio da caracterização topológica de rede, com relações de baixo nível, por meio de medidas de similaridade, em uma estrutura geral. Ainda no aprendizado supervisionado, as medidas de rede modularidade e centralidade Katz são aplicadas para classificar conjuntos de múltiplas observações, e um método de construção evolutiva de rede é aplicado ao problema de redução de dimensionalidade. O paradigma semissupervisionado é abordado por meio da extensão da heurística de facilidade de acesso para os casos em que apenas algumas amostras de dados rotuladas e muitas amostras não rotuladas estão disponíveis. É também proposta uma técnica semissupervisionada baseada em forças de interação, para a qual fornecemos heurísticas para selecionar parâmetros e uma análise de estabilidade mediante uma função de Lyapunov. Finalmente, uma técnica não supervisionada baseada em rede utiliza os conceitos de controle pontual e tempo de consenso de processos dinâmicos para derivar uma medida de similaridade usada para agrupar dados. Os dados são representados por uma rede conectada e esparsa na qual os vértices são elementos dinâmicos. Simulações com dados de referência e comparações com técnicas de aprendizado de máquina conhecidas são fornecidos para todas as técnicas propostas. As vantagens da representação de dados em rede e de processos dinâmicos para o aprendizado de máquina são evidenciadas em todos os casos
Книги з теми "Semi- and unsupervised learning"
Albalate, Amparo, and Wolfgang Minker. Semi-Supervised and Unsupervised Machine Learning. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557693.
Повний текст джерелаKyan, Matthew, Paisarn Muneesawang, Kambiz Jarrah, and Ling Guan. Unsupervised Learning. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118875568.
Повний текст джерелаCelebi, M. Emre, and Kemal Aydin, eds. Unsupervised Learning Algorithms. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8.
Повний текст джерелаLi, Xiangtao, and Ka-Chun Wong, eds. Natural Computing for Unsupervised Learning. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-98566-4.
Повний текст джерелаBaruque, Bruno. Fusion methods for unsupervised learning ensembles. Berlin: Springer, 2010.
Знайти повний текст джерелаBartlett, Marian Stewart. Face image analysis by unsupervised learning. Boston: Kluwer Academic Publishers, 2001.
Знайти повний текст джерелаLeordeanu, Marius. Unsupervised Learning in Space and Time. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42128-1.
Повний текст джерелаBaruque, Bruno, and Emilio Corchado. Fusion Methods for Unsupervised Learning Ensembles. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16205-3.
Повний текст джерелаBartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1637-8.
Повний текст джерелаBartlett, Marian Stewart. Face Image Analysis by Unsupervised Learning. Boston, MA: Springer US, 2001.
Знайти повний текст джерелаЧастини книг з теми "Semi- and unsupervised learning"
Taguchi, Y.-h. "PCA Based Unsupervised FE." In Unsupervised and Semi-Supervised Learning, 81–102. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22456-1_4.
Повний текст джерелаTaguchi, Y.-h. "TD Based Unsupervised FE." In Unsupervised and Semi-Supervised Learning, 103–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22456-1_5.
Повний текст джерелаM. Bagirov, Adil, Napsu Karmitsa, and Sona Taheri. "Introduction to Clustering." In Unsupervised and Semi-Supervised Learning, 3–13. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37826-4_1.
Повний текст джерелаM. Bagirov, Adil, Napsu Karmitsa, and Sona Taheri. "Performance and Evaluation Measures." In Unsupervised and Semi-Supervised Learning, 245–68. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37826-4_10.
Повний текст джерелаM. Bagirov, Adil, Napsu Karmitsa, and Sona Taheri. "Implementations and Data Sets." In Unsupervised and Semi-Supervised Learning, 269–79. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37826-4_11.
Повний текст джерелаM. Bagirov, Adil, Napsu Karmitsa, and Sona Taheri. "Numerical Experiments." In Unsupervised and Semi-Supervised Learning, 281–314. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37826-4_12.
Повний текст джерелаM. Bagirov, Adil, Napsu Karmitsa, and Sona Taheri. "Concluding Remarks." In Unsupervised and Semi-Supervised Learning, 315–17. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37826-4_13.
Повний текст джерелаM. Bagirov, Adil, Napsu Karmitsa, and Sona Taheri. "Theory of Nonsmooth Optimization." In Unsupervised and Semi-Supervised Learning, 15–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37826-4_2.
Повний текст джерелаM. Bagirov, Adil, Napsu Karmitsa, and Sona Taheri. "Nonsmooth Optimization Methods." In Unsupervised and Semi-Supervised Learning, 51–94. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37826-4_3.
Повний текст джерелаM. Bagirov, Adil, Napsu Karmitsa, and Sona Taheri. "Optimization Models in Cluster Analysis." In Unsupervised and Semi-Supervised Learning, 97–133. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37826-4_4.
Повний текст джерелаТези доповідей конференцій з теми "Semi- and unsupervised learning"
Hong, Xianbin, Gautam Pal, Sheng-Uei Guan, Prudence Wong, Dawei Liu, Ka Lok Man, and Xin Huang. "Semi-Unsupervised Lifelong Learning for Sentiment Classification." In HPCCT 2019: 2019 The 3rd High Performance Computing and Cluster Technologies Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341069.3342992.
Повний текст джерелаAndo, Shin. "Session details: Unsupervised and semi-supervised learning." In CIKM '11: International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2011. http://dx.doi.org/10.1145/3244893.
Повний текст джерелаSaini, Nikhil, Drumil Trivedi, Shreya Khare, Tejas Dhamecha, Preethi Jyothi, Samarth Bharadwaj, and Pushpak Bhattacharyya. "Disfluency Correction using Unsupervised and Semi-supervised Learning." In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021. http://dx.doi.org/10.18653/v1/2021.eacl-main.299.
Повний текст джерелаBreve, Fabricio Aparecido, and Daniel Carlos Guimaraes Pedronette. "Combined unsupervised and semi-supervised learning for data classification." In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2016. http://dx.doi.org/10.1109/mlsp.2016.7738877.
Повний текст джерелаSaul, L. K. "Graph-based methods for unsupervised and semi-supervised learning." In IEEE Workshop on Automatic Speech Recognition and Understanding, 2005. IEEE, 2005. http://dx.doi.org/10.1109/asru.2005.1566469.
Повний текст джерелаNie, Feiping, Hua Wang, Heng Huang, and Chris Ding. "Unsupervised and semi-supervised learning via ℓ1-norm graph." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126506.
Повний текст джерелаEscalante, Diego Alonso Chavez, Gabriel Taubin, Luis Gustavo Nonato, and Siome Klein Goldenstein. "Using Unsupervised Learning for Graph Construction in Semi-supervised Learning with Graphs." In 2013 XXVI SIBGRAPI - Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 2013. http://dx.doi.org/10.1109/sibgrapi.2013.13.
Повний текст джерелаHallett, Nicole, Kai Yi, Josef Dick, Christopher Hodge, Gerard Sutton, Yu Guang Wang, and Jingjing You. "Deep Learning Based Unsupervised and Semi-supervised Classification for Keratoconus." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206694.
Повний текст джерелаBai, Shuanhu, Chien-Lin Huang, Bin Ma, and Haizhou Li. "Semi-supervised learning of language model using unsupervised topic model." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5494940.
Повний текст джерелаLevow, Gina-Anne. "Unsupervised and semi-supervised learning of tone and pitch accent." In the main conference. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1220835.1220864.
Повний текст джерелаЗвіти організацій з теми "Semi- and unsupervised learning"
Tran, Anh, Theron Rodgers, and Timothy Wildey. Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1673174.
Повний текст джерелаVesselinov, Velimir Valentinov. TensorDecompostions : Unsupervised machine learning methods. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1493534.
Повний текст джерелаSprechmann, Pablo, and Guillermo Sapiro. Dictionary Learning and Sparse Coding for Unsupervised Clustering. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada513140.
Повний текст джерелаVesselinov, Velimir, Bulbul Ahmmed, Maruti Mudunuru, Jeff Pepin, Erick Burns, D. Siler, Satish Karra, and Richard Middleton. Discovering Hidden Geothermal Signatures using Unsupervised Machine Learning. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1781347.
Повний текст джерелаSafta, Cosmin, Habib Najm, Michael Grant, and Michael Sparapany. Trajectory Optimization via Unsupervised Probabilistic Learning On Manifolds. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1821958.
Повний текст джерелаAhmmed, Bulbul. Supervised and Unsupervised Machine Learning to Understanding Reactive-transport Data. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1630844.
Повний текст джерелаObert, James, and Timothy James Loffredo. Efficient Binary Static Code Data Flow Analysis Using Unsupervised Learning. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1592974.
Повний текст джерелаYeamans, Katelyn Angela. Unsupervised Machine Learning for Evaluation of Aging in Explosive Pressed Pellets. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1484618.
Повний текст джерелаWehner, Michael, Mark Risser, Paul Ullrich, and Shiheng Duan. Exploring variability in seasonal average and extreme precipitation using unsupervised machine learning. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769708.
Повний текст джерелаAdams, Jason, Katherine Goode, Joshua Michalenko, Phillip Lewis, and Daniel Ries. Semi-supervised Bayesian Low-shot Learning. Office of Scientific and Technical Information (OSTI), September 2021. http://dx.doi.org/10.2172/1821543.
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