Zeitschriftenartikel zum Thema „Hyperparameter search“
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Florea, Adrian-Catalin, und Razvan Andonie. „Weighted Random Search for Hyperparameter Optimization“. International Journal of Computers Communications & Control 14, Nr. 2 (14.04.2019): 154–69. http://dx.doi.org/10.15837/ijccc.2019.2.3514.
Ghawi, Raji, und Jürgen Pfeffer. „Efficient Hyperparameter Tuning with Grid Search for Text Categorization using kNN Approach with BM25 Similarity“. Open Computer Science 9, Nr. 1 (08.08.2019): 160–80. http://dx.doi.org/10.1515/comp-2019-0011.
Yang, Eun-Suk, Jong Dae Kim, Chan-Young Park, Hye-Jeong Song und Yu-Seop Kim. „Hyperparameter tuning for hidden unit conditional random fields“. Engineering Computations 34, Nr. 6 (07.08.2017): 2054–62. http://dx.doi.org/10.1108/ec-11-2015-0350.
Wen, Long, Xingchen Ye und Liang Gao. „A new automatic machine learning based hyperparameter optimization for workpiece quality prediction“. Measurement and Control 53, Nr. 7-8 (21.07.2020): 1088–98. http://dx.doi.org/10.1177/0020294020932347.
Hinz, Tobias, Nicolás Navarro-Guerrero, Sven Magg und Stefan Wermter. „Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks“. International Journal of Computational Intelligence and Applications 17, Nr. 02 (Juni 2018): 1850008. http://dx.doi.org/10.1142/s1469026818500086.
Yang, Zeshi, und Zhiqi Yin. „Efficient Hyperparameter Optimization for Physics-based Character Animation“. Proceedings of the ACM on Computer Graphics and Interactive Techniques 4, Nr. 1 (26.04.2021): 1–19. http://dx.doi.org/10.1145/3451254.
Han, Junjie, Cedric Gondro und Juan Steibel. „98 Using differential evolution to improve predictive accuracy of deep learning models applied to pig production data“. Journal of Animal Science 98, Supplement_3 (02.11.2020): 27. http://dx.doi.org/10.1093/jas/skaa054.048.
Tsai, Chun-Wei, und Zhi-Yan Fang. „An Effective Hyperparameter Optimization Algorithm for DNN to Predict Passengers at a Metro Station“. ACM Transactions on Internet Technology 21, Nr. 2 (30.03.2021): 1–24. http://dx.doi.org/10.1145/3410156.
Contreras, Pablo, Johanna Orellana-Alvear, Paul Muñoz, Jörg Bendix und Rolando Célleri. „Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment“. Atmosphere 12, Nr. 2 (10.02.2021): 238. http://dx.doi.org/10.3390/atmos12020238.
Jervis, Michael, Mingliang Liu und Robert Smith. „Deep learning network optimization and hyperparameter tuning for seismic lithofacies classification“. Leading Edge 40, Nr. 7 (Juli 2021): 514–23. http://dx.doi.org/10.1190/tle40070514.1.
Grakova, Ekaterina, Martin Golasowski, Roberto Montemanni, Kateřina Slaninová, Jan Martinovič, Jafar Jamal, Kateřina Janurová und Matteo Salani. „Hyperparameter search in periodic vehicle routing problem“. MATEC Web of Conferences 259 (2019): 01003. http://dx.doi.org/10.1051/matecconf/201925901003.
Ma, Jun Yan, Xiao Ping Liao, Wei Xia und Xue Lian Yan. „Hyperparameter Estimation Based on Gaussian Process and its Application in Injection Molding“. Advanced Materials Research 328-330 (September 2011): 524–29. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.524.
Goh, Rui Ying, Lai Soon Lee, Hsin-Vonn Seow und Kathiresan Gopal. „Hybrid Harmony Search–Artificial Intelligence Models in Credit Scoring“. Entropy 22, Nr. 9 (04.09.2020): 989. http://dx.doi.org/10.3390/e22090989.
Qin, Chao, Yunfeng Zhang, Fangxun Bao, Caiming Zhang, Peide Liu und Peipei Liu. „XGBoost Optimized by Adaptive Particle Swarm Optimization for Credit Scoring“. Mathematical Problems in Engineering 2021 (23.03.2021): 1–18. http://dx.doi.org/10.1155/2021/6655510.
Badriyah, Tessy, Dimas Bagus Santoso, Iwan Syarif und Daisy Rahmania Syarif. „Improving stroke diagnosis accuracy using hyperparameter optimized deep learning“. International Journal of Advances in Intelligent Informatics 5, Nr. 3 (17.11.2019): 256. http://dx.doi.org/10.26555/ijain.v5i3.427.
Hashi, Emrana Kabir, und Md. Shahid Uz Zaman. „Developing a Hyperparameter Tuning Based Machine Learning Approach of Heart Disease Prediction“. Journal of Applied Science & Process Engineering 7, Nr. 2 (30.10.2020): 631–47. http://dx.doi.org/10.33736/jaspe.2639.2020.
Chatzimparmpas, A., R. M. Martins, K. Kucher und A. Kerren. „VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization“. Computer Graphics Forum 40, Nr. 3 (Juni 2021): 201–14. http://dx.doi.org/10.1111/cgf.14300.
Beck, Daniel, Trevor Cohn, Christian Hardmeier und Lucia Specia. „Learning Structural Kernels for Natural Language Processing“. Transactions of the Association for Computational Linguistics 3 (Dezember 2015): 461–73. http://dx.doi.org/10.1162/tacl_a_00151.
Suto, Jozsef. „The effect of hyperparameter search on artificial neural network in human activity recognition“. Open Computer Science 11, Nr. 1 (01.01.2021): 411–22. http://dx.doi.org/10.1515/comp-2020-0227.
Panichella, Annibale. „A Systematic Comparison of search-Based approaches for LDA hyperparameter tuning“. Information and Software Technology 130 (Februar 2021): 106411. http://dx.doi.org/10.1016/j.infsof.2020.106411.
Marques, Pedro, Matilda Rhode und Ilir Gashi. „Waste not: Using diverse neural networks from hyperparameter search for improved malware detection“. Computers & Security 108 (September 2021): 102339. http://dx.doi.org/10.1016/j.cose.2021.102339.
Kapočiūtė-Dzikienė, Jurgita, Kaspars Balodis und Raivis Skadiņš. „Intent Detection Problem Solving via Automatic DNN Hyperparameter Optimization“. Applied Sciences 10, Nr. 21 (22.10.2020): 7426. http://dx.doi.org/10.3390/app10217426.
Soper, Daniel S. „Greed Is Good: Rapid Hyperparameter Optimization and Model Selection Using Greedy k-Fold Cross Validation“. Electronics 10, Nr. 16 (16.08.2021): 1973. http://dx.doi.org/10.3390/electronics10161973.
Kang, Seokho. „k-Nearest Neighbor Learning with Graph Neural Networks“. Mathematics 9, Nr. 8 (10.04.2021): 830. http://dx.doi.org/10.3390/math9080830.
Liu, Ning, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang und Jieping Ye. „AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 04 (03.04.2020): 4876–83. http://dx.doi.org/10.1609/aaai.v34i04.5924.
Schaer, Roger, Henning Müller und Adrien Depeursinge. „Optimized Distributed Hyperparameter Search and Simulation for Lung Texture Classification in CT Using Hadoop“. Journal of Imaging 2, Nr. 2 (07.06.2016): 19. http://dx.doi.org/10.3390/jimaging2020019.
Bouktif, Salah, Ali Fiaz, Ali Ouni und Mohamed Adel Serhani. „Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting“. Energies 13, Nr. 2 (13.01.2020): 391. http://dx.doi.org/10.3390/en13020391.
Le Dinh Phu, Cuong, und Dong Wang. „A Comparison of Machine Learning Methods to Predict Hospital Readmission of Diabetic Patient“. Asia Proceedings of Social Sciences 7, Nr. 2 (28.03.2021): 164–68. http://dx.doi.org/10.31580/apss.v7i2.1807.
RADIUK, P. „AN APPROACH TO ACCELERATE THE TRAINING OF CONVOLUTIONAL NEURAL NETWORKS BY TUNING THE HYPERPARAMETERS OF LEARNING“. Computer Systems and Information Technologies 2, Nr. 2 (03.11.2020): 32–37. http://dx.doi.org/10.31891/csit-2020-2-5.
Choi, Jung Chan, Zhongqiang Liu, Suzanne Lacasse und Elin Skurtveit. „Leak-Off Pressure Using Weakly Correlated Geospatial Information and Machine Learning Algorithms“. Geosciences 11, Nr. 4 (19.04.2021): 181. http://dx.doi.org/10.3390/geosciences11040181.
Wang, Yufei, Haiyang Zhang, Yongli An, Zhanlin Ji und Ivan Ganchev. „RG Hyperparameter Optimization Approach for Improved Indirect Prediction of Blood Glucose Levels by Boosting Ensemble Learning“. Electronics 10, Nr. 15 (27.07.2021): 1797. http://dx.doi.org/10.3390/electronics10151797.
Haqmi Abas, Mohamad Aqib. „Agarwood Oil Quality Classification using Support Vector Classifier and Grid Search Cross Validation Hyperparameter Tuning“. International Journal of Emerging Trends in Engineering Research 8, Nr. 6 (25.06.2020): 2551–56. http://dx.doi.org/10.30534/ijeter/2020/55862020.
Lin, Nan, Yongliang Chen, Haiqi Liu und Hanlin Liu. „A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity“. Minerals 11, Nr. 2 (03.02.2021): 159. http://dx.doi.org/10.3390/min11020159.
Lee, Sanghyeop, Junyeob Kim, Hyeon Kang, Do-Young Kang und Jangsik Park. „Genetic Algorithm Based Deep Learning Neural Network Structure and Hyperparameter Optimization“. Applied Sciences 11, Nr. 2 (14.01.2021): 744. http://dx.doi.org/10.3390/app11020744.
Kim, Seong-Hoon, Zong Woo Geem und Gi-Tae Han. „Hyperparameter Optimization Method Based on Harmony Search Algorithm to Improve Performance of 1D CNN Human Respiration Pattern Recognition System“. Sensors 20, Nr. 13 (01.07.2020): 3697. http://dx.doi.org/10.3390/s20133697.
Mallak, Ahlam, und Madjid Fathi. „A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest“. Sci 2, Nr. 3 (06.08.2020): 61. http://dx.doi.org/10.3390/sci2030061.
Mallak, Ahlam, und Madjid Fathi. „A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest“. Sci 2, Nr. 4 (24.09.2020): 61. http://dx.doi.org/10.3390/sci2040061.
Mallak, Ahlam, und Madjid Fathi. „A Hybrid Approach: Dynamic Diagnostic Rules for Sensor Systems in Industry 4.0 Generated by Online Hyperparameter Tuned Random Forest“. Sci 2, Nr. 4 (09.10.2020): 75. http://dx.doi.org/10.3390/sci2040075.
Utsugi, Akio, und Toru Kumagai. „Bayesian Analysis of Mixtures of Factor Analyzers“. Neural Computation 13, Nr. 5 (01.05.2001): 993–1002. http://dx.doi.org/10.1162/08997660151134299.
Lee, Woo-Young, Seung-Min Park und Kwee-Bo Sim. „Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm“. Optik 172 (November 2018): 359–67. http://dx.doi.org/10.1016/j.ijleo.2018.07.044.
Nahhas, Faten Hamed, Helmi Z. M. Shafri, Maher Ibrahim Sameen, Biswajeet Pradhan und Shattri Mansor. „Deep Learning Approach for Building Detection Using LiDAR–Orthophoto Fusion“. Journal of Sensors 2018 (05.08.2018): 1–12. http://dx.doi.org/10.1155/2018/7212307.
Chen, Yi-Wei, Qingquan Song und Xia Hu. „Techniques for Automated Machine Learning“. ACM SIGKDD Explorations Newsletter 22, Nr. 2 (17.01.2021): 35–50. http://dx.doi.org/10.1145/3447556.3447567.
Almaslukh, Bandar. „A Lightweight Deep Learning-Based Pneumonia Detection Approach for Energy-Efficient Medical Systems“. Wireless Communications and Mobile Computing 2021 (21.04.2021): 1–14. http://dx.doi.org/10.1155/2021/5556635.
Grakova, Ekaterina, Jan Martinovič, Kateřina Slaninová, Kateřina Janurová, Vojtěch Cima, Martin Golasowski, Roberto Montemanni und Matteo Salani. „Setting the Configuration Parameters of the Algorithm for the Periodic Vehicle Routing Problem by HPC Power“. MATEC Web of Conferences 296 (2019): 01009. http://dx.doi.org/10.1051/matecconf/201929601009.
Song, Shuang, Shugang Li, Tianjun Zhang, Li Ma, Shaobo Pan und Lu Gao. „Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN“. Energies 14, Nr. 5 (03.03.2021): 1384. http://dx.doi.org/10.3390/en14051384.
Schutera, Mark, Stefan Elser, Jochen Abhau, Ralf Mikut und Markus Reischl. „Strategies for supplementing recurrent neural network training for spatio-temporal prediction“. at - Automatisierungstechnik 67, Nr. 7 (26.07.2019): 545–56. http://dx.doi.org/10.1515/auto-2018-0124.
Huang, Jianzhong, Yuwan Cen, Nenggang Xie und Xiaohua Ye. „Inverse calculation of demolition robot based on gravitational search algorithm and differential evolution neural network“. International Journal of Advanced Robotic Systems 17, Nr. 3 (01.05.2020): 172988142092529. http://dx.doi.org/10.1177/1729881420925298.
Demidova, Liliya A., und Artyom V. Gorchakov. „Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods“. Algorithms 13, Nr. 4 (03.04.2020): 85. http://dx.doi.org/10.3390/a13040085.
Tuggener, Lukas, Mohammadreza Amirian, Fernando Benites, Pius von Däniken, Prakhar Gupta, Frank-Peter Schilling und Thilo Stadelmann. „Design Patterns for Resource-Constrained Automated Deep-Learning Methods“. AI 1, Nr. 4 (06.11.2020): 510–38. http://dx.doi.org/10.3390/ai1040031.
Tam, Lydia, Wasif Bala, Jonathan Lavezo, Seth Lummus, Hannes Vogel und Kristen Yeom. „PATH-06. IMAGE-BASED MACHINE LEARNING CLASSIFIER FOR PEDIATRIC POSTERIOR FOSSA TUMOR HISTOPATHOLOGY“. Neuro-Oncology 22, Supplement_3 (01.12.2020): iii425. http://dx.doi.org/10.1093/neuonc/noaa222.642.