Relevant bibliographies by topics / Deep extreme learning machine

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep extreme learning machine'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Deep extreme learning machine"

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Wang, Tianlei, Jiuwen Cao, Xiaoping Lai, and Badong Chen. "Deep Weighted Extreme Learning Machine." Cognitive Computation 10, no. 6 (2018): 890–907. http://dx.doi.org/10.1007/s12559-018-9602-9.

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Mercaldo, Francesco, Luca Brunese, Fabio Martinelli, Antonella Santone, and Mario Cesarelli. "Experimenting with Extreme Learning Machine for Biomedical Image Classification." Applied Sciences 13, no. 14 (2023): 8558. http://dx.doi.org/10.3390/app13148558.

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Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently emerged which, as shown in experimental studies, can produce an acceptable predictive performance in several classification tasks, and at a much lower training cost compared to deep learning networks that are trained by backpropagation. We propose a method devoted to exploring the possibility
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Jiang, X. W., T. H. Yan, J. J. Zhu, et al. "Densely Connected Deep Extreme Learning Machine Algorithm." Cognitive Computation 12, no. 5 (2020): 979–90. http://dx.doi.org/10.1007/s12559-020-09752-2.

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Ding, Shifei, Nan Zhang, Xinzheng Xu, Lili Guo, and Jian Zhang. "Deep Extreme Learning Machine and Its Application in EEG Classification." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/129021.

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Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We c
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Cheng, Xiangyi, Huaping Liu, Xinying Xu, and Fuchun Sun. "Denoising deep extreme learning machine for sparse representation." Memetic Computing 9, no. 3 (2016): 199–212. http://dx.doi.org/10.1007/s12293-016-0185-2.

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Chu, Yunfei, Chunyan Feng, Caili Guo, and Yaqing Wang. "Network embedding based on deep extreme learning machine." International Journal of Machine Learning and Cybernetics 10, no. 10 (2018): 2709–24. http://dx.doi.org/10.1007/s13042-018-0895-5.

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Sasou, Akira. "Deep Residual Learning With Dilated Causal Convolution Extreme Learning Machine." IEEE Access 9 (2021): 165708–18. http://dx.doi.org/10.1109/access.2021.3134700.

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Man, Zhihong, and Guang-Bin Huang. "Special issue on extreme learning machine and deep learning networks." Neural Computing and Applications 32, no. 18 (2020): 14241–45. http://dx.doi.org/10.1007/s00521-020-05175-0.

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Ding, Shifei, Lili Guo, and Yanlu Hou. "Extreme learning machine with kernel model based on deep learning." Neural Computing and Applications 28, no. 8 (2016): 1975–84. http://dx.doi.org/10.1007/s00521-015-2170-y.

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Phumrattanaprapin, Khanittha, and Punyaphol Horata. "Extended Hierarchical Extreme Learning Machine with Multilayer Perceptron." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 10, no. 2 (2017): 196–204. http://dx.doi.org/10.37936/ecti-cit.2016102.68266.

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The Deep Learning approach provides a high performance of classification, especially when invoking image classification problems. However, a shortcoming of the traditional Deep Learning method is the large time scale of training. The hierarchical extreme learning machine (H-ELM) framework was based on the hierarchical learning architecture of multilayer perceptron to address the problem. H-ELM is composed of two parts; the first entails unsupervised multilayer encoding, and the second is the supervised feature classification. H-ELM can give a higher accuracy rate than the traditional ELM. Howe
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Dissertations / Theses on the topic "Deep extreme learning machine"

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Franch, Gabriele. "Deep Learning for Spatiotemporal Nowcasting." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/295096.

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Nowcasting – short-term forecasting using current observations – is a key challenge that human activities have to face on a daily basis. We heavily rely on short-term meteorological predictions in domains such as aviation, agriculture, mobility, and energy production. One of the most important and challenging task for meteorology is the nowcasting of extreme events, whose anticipation is highly needed to mitigate risk in terms of social or economic costs and human safety. The goal of this thesis is to contribute with new machine learning methods to improve the spatio-temporal precision of now
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Franch, Gabriele. "Deep Learning for Spatiotemporal Nowcasting." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/295096.

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Nowcasting – short-term forecasting using current observations – is a key challenge that human activities have to face on a daily basis. We heavily rely on short-term meteorological predictions in domains such as aviation, agriculture, mobility, and energy production. One of the most important and challenging task for meteorology is the nowcasting of extreme events, whose anticipation is highly needed to mitigate risk in terms of social or economic costs and human safety. The goal of this thesis is to contribute with new machine learning methods to improve the spatio-temporal precision of now
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Wong, Chi Man. "Extreme learning machine for multi-class classification." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948432.

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He, Fengxiang. "Theoretical Deep Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25674.

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Deep learning has long been criticised as a black-box model for lacking sound theoretical explanation. During the PhD course, I explore and establish theoretical foundations for deep learning. In this thesis, I present my contributions positioned upon existing literature: (1) analysing the generalizability of the neural networks with residual connections via complexity and capacity-based hypothesis complexity measures; (2) modeling stochastic gradient descent (SGD) by stochastic differential equations (SDEs) and their dynamics, and further characterizing the generalizability of deep learning;
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Grzeidak, Emerson. "Identification of nonlinear systems based on extreme learning machine." reponame:Repositório Institucional da UnB, 2016. http://repositorio.unb.br/handle/10482/21603.

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Dissertação (mestrado)—Universidade de Brasília, Programa de Pós-Graduação em Sistemas Mecatrônicos, 2016.<br>Submitted by Camila Duarte (camiladias@bce.unb.br) on 2016-09-14T17:33:55Z No. of bitstreams: 1 2016_EmersonGrzeidak.pdf: 5274560 bytes, checksum: 0f649b217c325601c125fad908bc164f (MD5)<br>Approved for entry into archive by Raquel Viana(raquelviana@bce.unb.br) on 2016-10-21T18:14:35Z (GMT) No. of bitstreams: 1 2016_EmersonGrzeidak.pdf: 5274560 bytes, checksum: 0f649b217c325601c125fad908bc164f (MD5)<br>Made available in DSpace on 2016-10-21T18:14:35Z (GMT). No. of bitstreams: 1 2016_Em
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Fan, Shuangfei. "Deep Representation Learning on Labeled Graphs." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/96596.

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We introduce recurrent collective classification (RCC), a variant of ICA analogous to recurrent neural network prediction. RCC accommodates any differentiable local classifier and relational feature functions. We provide gradient-based strategies for optimizing over model parameters to more directly minimize the loss function. In our experiments, this direct loss minimization translates to improved accuracy and robustness on real network data. We demonstrate the robustness of RCC in settings where local classification is very noisy, settings that are particularly challenging for ICA. As a new
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Zhuang, Zhongfang. "Deep Learning on Attributed Sequences." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/507.

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Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length. However, in many real-world applications, the data exists in the form of attributed sequences, which is composed of a set of fixed-size attributes and variable-length sequences with dependencies between them. In the attributed sequence context, feature learning remains challenging due to the dependencies between sequences and their associated attributes. In this dissertation, we focus on analyzing and building deep learning models for
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FRACCAROLI, MICHELE. "Explainable Deep Learning." Doctoral thesis, Università degli studi di Ferrara, 2023. https://hdl.handle.net/11392/2503729.

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Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelli
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Riva, Mateus. "Spatial Relational Reasoning in Machine Learning : Deep Learning and Graph Clustering." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT043.

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Cette thèse étudie les capacités des méthodes d'apprentissage automatique à raisonner sur des relations spatiales, en particulier sur les relations directionnelles, et l'utilisation de connaissances relationnelles, connues a priori, par ces méthodes. Il existe de nombreux travaux dans le domaine de l'exploitation de connaissances sur les relations dans des méthodes d'apprentissage automatique. Cependant, ce corpus de travaux laisse encore plusieurs questions ouvertes. Tout au long de cette thèse, nous explorons, étudions et tentons d'expliquer différentes questions de recherche liées à ces que
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Elmarakeby, Haitham Abdulrahman. "Deep Learning for Biological Problems." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/86264.

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The last decade has witnessed a tremendous increase in the amount of available biological data. Different technologies for measuring the genome, epigenome, transcriptome, proteome, metabolome, and microbiome in different organisms are producing large amounts of high-dimensional data every day. High-dimensional data provides unprecedented challenges and opportunities to gain a better understanding of biological systems. Unlike other data types, biological data imposes more constraints on researchers. Biologists are not only interested in accurate predictive models that capture complex input-out
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Books on the topic "Deep extreme learning machine"

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Hu, Fei, and Xiali Hei. AI, Machine Learning and Deep Learning. CRC Press, 2023. http://dx.doi.org/10.1201/9781003187158.

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Suriyan, Kannadhasan, Prasanna Devi Sivakumar, Paavai Gopalan Anand, and Durgadevi Palani, eds. Machine Learning, Deep Learning, and Blockchain. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88237-1.

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Rivera, Gilberto, Alejandro Rosete, Bernabé Dorronsoro, and Nelson Rangel-Valdez, eds. Innovations in Machine and Deep Learning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40688-1.

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Tsihrintzis, George A., Maria Virvou, and Lakhmi C. Jain, eds. Advances in Machine Learning/Deep Learning-based Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-76794-5.

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Hong, Huixiao, ed. Machine Learning and Deep Learning in Computational Toxicology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20730-3.

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Stamp, Mark, and Martin Jureček, eds. Machine Learning, Deep Learning and AI for Cybersecurity. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83157-7.

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Devi, K. Gayathri, Kishore Balasubramanian, and Le Anh Ngoc. Machine Learning and Deep Learning Techniques for Medical Science. CRC Press, 2022. http://dx.doi.org/10.1201/9781003217497.

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Abualigah, Laith, ed. Classification Applications with Deep Learning and Machine Learning Technologies. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17576-3.

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Borhani, Reza, Soheila Borhani, and Aggelos K. Katsaggelos. Fundamentals of Machine Learning and Deep Learning in Medicine. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19502-0.

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Pillai, Anitha S., and Bindu Menon. Machine Learning and Deep Learning in Neuroimaging Data Analysis. CRC Press, 2024. http://dx.doi.org/10.1201/9781003264767.

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Book chapters on the topic "Deep extreme learning machine"

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Yahia, Siwar, Salwa Said, and Mourad Zaied. "Deep Wavelet Extreme Learning Machine for Data Classification." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20005-3_11.

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Qi, Ya-Li, and Ye-Li Li. "Deep Representation Based on Multilayer Extreme Learning Machine." In Electronics, Communications and Networks V. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0740-8_17.

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Tissera, Migel D., and Mark D. McDonnell. "Deep Extreme Learning Machines for Classification." In Proceedings of ELM-2014 Volume 1. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14063-6_29.

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Wei, Jie, Huaping Liu, Gaowei Yan, and Fuchun Sun. "Multi-modal Deep Extreme Learning Machine for Robotic Grasping Recognition." In Proceedings in Adaptation, Learning and Optimization. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28373-9_19.

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Law, Anwesha, Ratula Ray, and Ashish Ghosh. "Autoencoder and Extreme Learning Machine Based Deep Multi-label Classifier." In Lecture Notes in Computer Science. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-12700-7_17.

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Cheng, Xiangyi, Huaping Liu, Xinying Xu, and Fuchun Sun. "Denoising Deep Extreme Learning Machines for Sparse Representation." In Proceedings in Adaptation, Learning and Optimization. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28373-9_20.

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Cao, Ruimin, Fengli Wang, and Lina Hao. "Extreme Learning Machine Based Modified Deep Auto-Encoder Network Classifier Algorithm." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5230-9_19.

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Zeng, Yujun, Xin Xu, Yuqiang Fang, and Kun Zhao. "Traffic Sign Recognition Using Deep Convolutional Networks and Extreme Learning Machine." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23989-7_28.

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Bartz-Beielstein, Thomas, and Martin Zaefferer. "Models." In Hyperparameter Tuning for Machine and Deep Learning with R. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5170-1_3.

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AbstractThis chapter presents a unique overview and a comprehensive explanation of Machine Learning (ML) and Deep Learning (DL) methods. Frequently used ML and DL methods; their hyperparameter configurations; and their features such as types, their sensitivity, and robustness, as well as heuristics for their determination, constraints, and possible interactions are presented. In particular, we cover the following methods: $$k$$ k -Nearest Neighbor (KNN), Elastic Net (EN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and DL. This cha
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Dan, Songjian. "Spectral Identification Model of NIR Origin Based on Deep Extreme Learning Machine." In The 2021 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89508-2_7.

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Conference papers on the topic "Deep extreme learning machine"

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Agarwal, Kishan, Srinivasulu Chennupalli, Rishi Kolluru, and Hamanpure Vaibhav. "Predictive Modeling of Air Quality: Performance Comparison of Supervised Machine Learning, Deep Learning, and Extreme Learning Machines." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10984959.

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Hemantkumar, Parekh Ved, Biswajeet Samal, Rudra Narayan Panda, Sk Shahid Akhtar, Debendra Muduli, and Santosh Kumar Sharma. "Enhanced Skin Cancer Detection Model: A Deep Learning Feature Fusion with Extreme Learning Machine Approach." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725357.

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Chengjun, Xu, Shu Sheng, and Wang Kun. "Rolling Bearing Fault Diagnosis Based on Improved Multi-Scale Dispersion Entropy and Deep Extreme Learning Machine." In 2024 International Conference on Automation in Manufacturing, Transportation and Logistics (ICaMaL). IEEE, 2024. https://doi.org/10.1109/icamal62577.2024.10919637.

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V, Rahul Chiranjeevi, and Malathi D. "Identification of Abnormal Activities in Surveillance Scenes using Deep Auto Encoder and Sequential Extreme Learning Machine." In 2025 International Conference on Visual Analytics and Data Visualization (ICVADV). IEEE, 2025. https://doi.org/10.1109/icvadv63329.2025.10961503.

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Vardhan Reddy, Vangireddy Vishnu, Uma Priyadarsini P. S, and Saroj Kumar Tiwari. "Expression of Concern for: Precise Medical Diagnosis For Brain Tumor Detection and Data Sample Imbalance Analysis using Enhanced Kernel Extreme Learning Machine Model with Deep Belief Network Compared to Extreme Machine Learning." In 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS). IEEE, 2022. http://dx.doi.org/10.1109/macs56771.2022.10703541.

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Xu, Junyang, Qiwei Zhang, Yuexin Liu, Xuefeng Wang, and Chu Zhang. "Wind Power Prediction based on Multivariate and Convolutional Neural Network-Fast Deep Stacked Network Extreme Learning Machine." In 2024 IEEE International Conference on Cognitive Computing and Complex Data (ICCD). IEEE, 2024. https://doi.org/10.1109/iccd62811.2024.10843455.

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Li, Zhuangzi, Shan Liu, and Ge Li. "PointELM: Fast Point Cloud Classification Using Deep Random Mapping Based Extreme Learning Machines." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687460.

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Liang, Bohao, Xiangyu Kong, Jidong Wang, Mingxuan Lu, Longyu Zhang, and Mao Liu. "A Capacity Planning Method for EV Charging Stations Based on Fuzzy C-Means Clustering and Deep Extreme Learning Machine." In 2024 27th International Conference on Electrical Machines and Systems (ICEMS). IEEE, 2024. https://doi.org/10.23919/icems60997.2024.10921223.

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Suprano, Alessia, Danilo Zia, Luca Innocenti, et al. "Photonic quantum extreme learning machine." In Quantum 2.0. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/quantum.2024.qw4a.2.

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We experimentally implemented a quantum extreme learning machine to re-construct the polarization state of single photons. Our approach offers a resource-efficient method that does not require a detailed apparatus calibration.
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Guo, Xuqi, Yusong Pang, Gaowei Yan, and Tiezhu Qiao. "Time series forecasting based on deep extreme learning machine." In 2017 29th Chinese Control And Decision Conference (CCDC). IEEE, 2017. http://dx.doi.org/10.1109/ccdc.2017.7978277.

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Reports on the topic "Deep extreme learning machine"

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Choudhary, Alok, Ankit Agrawal, and Wei-Keng Liao. PROTEUS: Machine Learning Driven Resilience for Extreme-scale Systems. Office of Scientific and Technical Information (OSTI), 2023. https://doi.org/10.2172/1998847.

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Vilalta, Ricardo. Modern Machine Learning Techniques. Instats Inc., 2024. http://dx.doi.org/10.61700/6sziq6usb3koe786.

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This workshop offers a comprehensively introduction to modern machine learning techniques in Python. Designed for PhD students, professors, and professional researchers, the seminar covers a variety of valuable techniques for machine learning, from meta-learning and transfer learning, to domain adaptation, active learning, deep learning, and Bayesian networks, equipping participants with key practical skills to enhance their research capabilities.
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Fessel, Kimberly. Machine Learning in Python. Instats Inc., 2024. http://dx.doi.org/10.61700/s74zy0ivgwioe1764.

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This intensive, hands-on workshop offers a deep dive into machine learning with Python, designed for PhD students, professors, and researchers across various fields. Participants will master practical skills in data cleaning, exploratory data analysis, and building powerful machine learning models, including neural networks, to elevate their research. With real-world coding exercises and expert guidance, this workshop will equip you with the tools to turn data into actionable insights.
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Ehrmann, Thomas, Mamikon Gulian, and Michael Weylandt. Improved Subseasonal Forecasting of Extreme Polar Vortices Using Machine Learning. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2430051.

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/3sz8pzpbpsg2i469.

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This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.
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Flaxman, Seth. Statistical Machine Learning for Researchers. Instats Inc., 2023. http://dx.doi.org/10.61700/wu1mihoap95h0469.

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This workshop is designed to empower researchers with the fundamentals of machine learning using R. Participants will learn the key principles that make machine learning so effective, powering the modern AI and deep learning revolution. Through hands-on exercises, participants will gain experience applying a variety of flexible and scalable statistical machine learning methods to analyze datasets and build effective predictive models. An official Instats certificate of completion is provided along with 2 ECTS Equivalent points.
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Gastelum, Zoe, Laura Matzen, Mallory Stites, et al. Assessing Cognitive Impacts of Errors from Machine Learning and Deep Learning Models: Final Report. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1821527.

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Wehner, 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), 2021. http://dx.doi.org/10.2172/1769708.

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Chen, Yiran, and Hai Li. SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme Scale Computing Platforms. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1846568.

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