Auswahl der wissenschaftlichen Literatur zum Thema „Hyperparameter search“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "Hyperparameter search" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Zeitschriftenartikel zum Thema "Hyperparameter search":
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.
Dissertationen zum Thema "Hyperparameter search":
Wang, Jiexin. „Policy Hyperparameter Exploration for Behavioral Learning of Smartphone Robots“. 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225744.
Gabere, Musa Nur. „Prediction of antimicrobial peptides using hyperparameter optimized support vector machines“. Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7345_1330684697.
Antimicrobial peptides (AMPs) play a key role in the innate immune response. They can be ubiquitously found in a wide range of eukaryotes including mammals, amphibians, insects, plants, and protozoa. In lower organisms, AMPs function merely as antibiotics by permeabilizing cell membranes and lysing invading microbes. Prediction of antimicrobial peptides is important because experimental methods used in characterizing AMPs are costly, time consuming and resource intensive and identification of AMPs in insects can serve as a template for the design of novel antibiotic. In order to fulfil this, firstly, data on antimicrobial peptides is extracted from UniProt, manually curated and stored into a centralized database called dragon antimicrobial peptide database (DAMPD). Secondly, based on the curated data, models to predict antimicrobial peptides are created using support vector machine with optimized hyperparameters. In particular, global optimization methods such as grid search, pattern search and derivative-free methods are utilised to optimize the SVM hyperparameters. These models are useful in characterizing unknown antimicrobial peptides. Finally, a webserver is created that will be used to predict antimicrobial peptides in haemotophagous insects such as Glossina morsitan and Anopheles gambiae.
Lundh, Felix, und Oscar Barta. „Hyperparameters relationship to the test accuracy of a convolutional neural network“. Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19846.
Myrberger, Axel, und Essen Benjamin Von. „Classifying True and Fake Telecommunication Signals With Deep Learning“. Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297675.
Målet med det här projektet var att klassificera artificiellt genererade signaler, falska, och riktiga, sanna, telekommunikation signaler med hjälp av signalernas frekvens- svar med djup inlärningsmetoder, deep learning. Ett annat mål med projektet var att klassificera signalerna med minsta möjliga antalet dimensioner av datan. Datasetet som användes bestod av till hälften av uppmät data som Ericsson har tillhandahållit, och till hälften av generad data ifrån en WINNER II modell implementerad i Matlab. En slutsats som kunde dras är att en normaliserad version av beloppet av det komplexa frekvenssvaret innehöll tillräckligt med information för att träna ett feedforward nätverk till att uppnå en hög klassificeringssäkerhet. För att vidare öka tillförlitligheten av nätverket gjordes en hyperparametersökning, detta ökade tillförligheten till 90 procent för testdataseten. Resultaten visar att det är möjligt för neurala nätverk att skilja mellan sanna och falska telekommunikations- signaler baserat på deras frekvenssvar, även om det är svårt för människor att skilja signalerna åt.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm
Stynsberg, John. „Incorporating Scene Depth in Discriminative Correlation Filters for Visual Tracking“. Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153110.
Buchteile zum Thema "Hyperparameter search":
Wistuba, Martin, Nicolas Schilling und Lars Schmidt-Thieme. „Hyperparameter Search Space Pruning – A New Component for Sequential Model-Based Hyperparameter Optimization“. In Machine Learning and Knowledge Discovery in Databases, 104–19. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_7.
Plate, Tony. „Controlling the hyperparameter search in MacKay’s Bayesian neural network framework“. In Lecture Notes in Computer Science, 93–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49430-8_5.
Plate, Tony. „Controlling the Hyperparameter Search in MacKay’s Bayesian Neural Network Framework“. In Lecture Notes in Computer Science, 91–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35289-8_7.
Ordozgoiti, Bruno, und Lluís A. Belanche Muñoz. „Off-the-Grid: Fast and Effective Hyperparameter Search for Kernel Clustering“. In Machine Learning and Knowledge Discovery in Databases, 399–415. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67661-2_24.
Florea, Adrian Cătălin, und Răzvan Andonie. „A Dynamic Early Stopping Criterion for Random Search in SVM Hyperparameter Optimization“. In IFIP Advances in Information and Communication Technology, 168–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92007-8_15.
Khamitov, Kamil, Nina Popova, Yuri Konkov und Tony Castillo. „Tuning ANNs Hyperparameters and Neural Architecture Search Using HPC“. In Communications in Computer and Information Science, 536–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64616-5_46.
Jääsaari, Elias, Ville Hyvönen und Teemu Roos. „Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search“. In Advances in Knowledge Discovery and Data Mining, 590–602. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16145-3_46.
Hinaut, Xavier, und Nathan Trouvain. „Which Hype for My New Task? Hints and Random Search for Echo State Networks Hyperparameters“. In Lecture Notes in Computer Science, 83–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86383-8_7.
Lopez, Kyra Mikaela M., und Ma Sheila A. Magboo. „A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers“. In Machine Learning and Artificial Intelligence. IOS Press, 2020. http://dx.doi.org/10.3233/faia200765.
Larsen, Kai R., und Daniel S. Becker. „Why Use Automated Machine Learning?“ In Automated Machine Learning for Business, 1–22. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190941659.003.0001.
Konferenzberichte zum Thema "Hyperparameter search":
Balaprakash, Prasanna, Michael Salim, Thomas D. Uram, Venkat Vishwanath und Stefan M. Wild. „DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks“. In 2018 IEEE 25th International Conference on High Performance Computing (HiPC). IEEE, 2018. http://dx.doi.org/10.1109/hipc.2018.00014.
Lopez-Ramos, Luis M., und Baltasar Beferull-Lozano. „Online Hyperparameter Search Interleaved with Proximal Parameter Updates“. In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287537.
Buratti, Benedetto J., und Eli Upfal. „Ordalia: Deep Learning Hyperparameter Search via Generalization Error Bounds Extrapolation“. In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006144.
Cho, Minsu, und Chinmay Hegde. „Reducing the Search Space for Hyperparameter Optimization Using Group Sparsity“. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682434.
Li, Zhenzhen, Lianwen Jin, Chunlin Yang und Zhuoyao Zhong. „Hyperparameter search for deep convolutional neural network using effect factors“. In 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2015. http://dx.doi.org/10.1109/chinasip.2015.7230511.
Sanchez, Odnan Ref, Matteo Repetto, Alessandro Carrega und Raffaele Bolla. „Evaluating ML-based DDoS Detection with Grid Search Hyperparameter Optimization“. In 2021 IEEE 7th International Conference on Network Softwarization (NetSoft). IEEE, 2021. http://dx.doi.org/10.1109/netsoft51509.2021.9492633.
Zhang, Michael, Chandra Krintz, Markus Mock und Rich Wolski. „Seneca: Fast and Low Cost Hyperparameter Search for Machine Learning Models“. In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, 2019. http://dx.doi.org/10.1109/cloud.2019.00071.
Wendt, Alexander, Marco Wuschnig und Martin Lechner. „Speeding up Common Hyperparameter Optimization Methods by a Two-Phase-Search“. In IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2020. http://dx.doi.org/10.1109/iecon43393.2020.9254801.
Shekar, B. H., und Guesh Dagnew. „Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data“. In 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP). IEEE, 2019. http://dx.doi.org/10.1109/icaccp.2019.8882943.
Alibrahim, Hussain, und Simone A. Ludwig. „Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization“. In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021. http://dx.doi.org/10.1109/cec45853.2021.9504761.