Academic literature on the topic 'Bootstrapping neural networks'
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Journal articles on the topic "Bootstrapping neural networks"
Franke, Jürgen, and Michael H. Neumann. "Bootstrapping Neural Networks." Neural Computation 12, no. 8 (August 1, 2000): 1929–49. http://dx.doi.org/10.1162/089976600300015204.
Full textLi, Xiangsheng, Yanghui Rao, Haoran Xie, Raymond Yiu Keung Lau, Jian Yin, and Fu Lee Wang. "Bootstrapping Social Emotion Classification with Semantically Rich Hybrid Neural Networks." IEEE Transactions on Affective Computing 8, no. 4 (October 1, 2017): 428–42. http://dx.doi.org/10.1109/taffc.2017.2716930.
Full textMistry, Sajib, Lie Qu, and Athman Bouguettaya. "Layer-based Composite Reputation Bootstrapping." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–28. http://dx.doi.org/10.1145/3448610.
Full textÁlvarez-Aparicio, Claudia, Ángel Manuel Guerrero-Higueras, Luis V. Calderita, Francisco J. Rodríguez-Lera, Vicente Matellán, and Camino Fernández-Llamas. "Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot." Applied Sciences 11, no. 21 (October 27, 2021): 10043. http://dx.doi.org/10.3390/app112110043.
Full textYan, Yilin, Min Chen, Saad Sadiq, and Mei-Ling Shyu. "Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters." International Journal of Multimedia Data Engineering and Management 8, no. 1 (January 2017): 1–20. http://dx.doi.org/10.4018/ijmdem.2017010101.
Full textBarth, R., J. IJsselmuiden, J. Hemming, and E. J. Van Henten. "Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation." Computers and Electronics in Agriculture 161 (June 2019): 291–304. http://dx.doi.org/10.1016/j.compag.2017.11.040.
Full textHsiao, Hsiao-Fen, Jiang-Chuan Huang, and Zheng-Wei Lin. "Portfolio construction using bootstrapping neural networks: evidence from global stock market." Review of Derivatives Research 23, no. 3 (July 25, 2019): 227–47. http://dx.doi.org/10.1007/s11147-019-09163-y.
Full textRichman, Ronald, and Mario V. Wüthrich. "Nagging Predictors." Risks 8, no. 3 (August 4, 2020): 83. http://dx.doi.org/10.3390/risks8030083.
Full textKUAN, MEI MING, CHEE PENG LIM, and ROBERT F. HARRISON. "ON OPERATING STRATEGIES OF THE FUZZY ARTMAP NEURAL NETWORK: A COMPARATIVE STUDY." International Journal of Computational Intelligence and Applications 03, no. 01 (March 2003): 23–43. http://dx.doi.org/10.1142/s1469026803000847.
Full textMedina, Oded, Roi Yozevitch, and Nir Shvalb. "Synthetic Sensor Array Training Sets for Neural Networks." Journal of Sensors 2019 (September 10, 2019): 1–10. http://dx.doi.org/10.1155/2019/9254315.
Full textDissertations / Theses on the topic "Bootstrapping neural networks"
Van, Lierde Boris. "Developing Box-Pushing Behaviours Using Evolutionary Robotics." Thesis, Högskolan Dalarna, Datateknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6250.
Full textLin, Zheng-Wei, and 林政緯. "Portfolio Construction Using Bootstrapping Neural Networks." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/50166282657736133905.
Full text雲林科技大學
管理研究所博士班
99
ABSTRACT Despite having become firmly established as one of the major cornerstone principles of modern finance, traditional Markowitz mean-variance analysis has, nevertheless, failed to gain widespread acceptance as a practical tool for equity management. The Markowitz optimization enigma essentially centers on the severe estimation risk associated with the input parameters, as well as the resultant financially irrelevant or even false optimal portfolios and asset allocation proposals. We therefore propose a portfolio construction method in the present study which incorporates the adoption of bootstrapping neural network architecture. In specific terms, a residual bootstrapping sample, which is derived from multilayer feedforward neural networks, is incorporated into the estimation of the expected returns and the covariance matrix, which are then, in turn, integrated into the traditional Markowitz optimization procedure. The efficacy of our proposed approach is illustrated by comparing it with traditional Markowitz mean-variance analysis, as well as the James-Stein and minimum-variance estimators, with the empirical results indicating that this novel approach significantly outperforms the benchmark models, in terms of various risk-adjusted performance measures. The evidence provided by this study suggests that this new approach has significant promise with regard to the enhancement of the investment value of Markowitz mean-variance analysis.
Book chapters on the topic "Bootstrapping neural networks"
Allende, Héctor, Ricardo Ñanculef, and Rodrigo Salas. "Robust Bootstrapping Neural Networks." In MICAI 2004: Advances in Artificial Intelligence, 813–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24694-7_84.
Full textZadel, Stefan. "An algorithm for bootstrapping the core of a biologically inspired motor control system." In Artificial Neural Networks — ICANN 96, 629–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_107.
Full textChillotti, Ilaria, Marc Joye, and Pascal Paillier. "Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks." In Lecture Notes in Computer Science, 1–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78086-9_1.
Full textAi, Na, Jinye Peng, Jun Wang, Lin Wang, and Jin Qi. "Single Image Super-Resolution by Learned Double Sparsity Dictionaries Combining Bootstrapping Method." In Artificial Neural Networks and Machine Learning – ICANN 2017, 565–73. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_64.
Full textYan, Yilin, Min Chen, Saad Sadiq, and Mei-Ling Shyu. "Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters." In Deep Learning and Neural Networks, 274–94. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch017.
Full textAk, R., V. Vitelli, and E. Zio. "Uncertainty modeling in wind power generation prediction by neural networks and bootstrapping." In Safety, Reliability and Risk Analysis, 3191–96. CRC Press, 2013. http://dx.doi.org/10.1201/b15938-483.
Full textLi, Fangjun, and Gao Niu. "US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model." In Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning, 177–207. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8455-2.ch007.
Full textConference papers on the topic "Bootstrapping neural networks"
Nguyen, Hung, Matthew Garratt, and Hussein Abbass. "Apprenticeship Bootstrapping." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489064.
Full textAlbert, Paul, Diego Ortego, Eric Arazo, Noel O'Connor, and Kevin McGuinness. "ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533616.
Full textKirstein, Stephan, Heiko Wersing, and Edgar Korner. "Towards autonomous bootstrapping for life-long learning categorization tasks." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596344.
Full textChai, Sek, Kilho Son, and Jesse Hostetler. "Bootstrapping Deep Neural Networks from Approximate Image Processing Pipelines." In 2019 2nd Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2). IEEE, 2019. http://dx.doi.org/10.1109/emc249363.2019.00009.
Full textAnirudh, Rushil, and Jayaraman J. Thiagarajan. "Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683547.
Full textKottur, Satwik, Xiaoyu Wang, and Vitor Carvalho. "Exploring Personalized Neural Conversational Models." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/521.
Full textYan, Lingyong, Xianpei Han, Ben He, and Le Sun. "Global Bootstrapping Neural Network for Entity Set Expansion." In Findings of the Association for Computational Linguistics: EMNLP 2020. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.findings-emnlp.331.
Full textVlaicu, Dan, and Mike Stojakovic. "Probabilistic Models to Approximate Highly Repetitive Linear and Nonlinear Finite Element Analyses of Nuclear Components." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77220.
Full textGrezl, Frantiseli, and Martin Karafiat. "Semi-supervised bootstrapping approach for neural network feature extractor training." In 2013 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2013. http://dx.doi.org/10.1109/asru.2013.6707775.
Full textde Peretti, Christian, and Carole Siani. "Bootstrapping tests for conditional heteroskedasticity based on artificial neural network." In Multiconference on "Computational Engineering in Systems Applications. IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.4281681.
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