Search Results - "Hyper-parameter Optimizations"
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Authors: et al.
Contributors: et al.
Source: Neural Computing and Applications. 37:24123-24148
Subject Terms: Hyperparameter Optimization, Box-shaped Member, Support Vector Regression, Timber, Performance Metrices, Learning Systems, Structural Analysis, Machine Learning, Naive Bayes, Timber Structures, Energy Dissipation, Learning Algorithms, Structural Members, Logistic Regression, Machine-learning, Hyper-parameter Optimizations, Model Performance Metric, Decision Trees, Timber Structure, Logistics Regressions, Discriminant Analysis, Modeling Performance, Classification, Structural Optimization, Gaussians, Nearest Neighbor Search, Model Performance Metrics, Forecasting
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2
Authors: İhsan Tuğal
Source: Energy Reports. 13:4247-4260
Subject Terms: Building Hvac System, Hyperparameter Optimization, Energy, Meta-heuristic Algorithms, Energy Efficiency, Gray Wolves, Entropy, Hvac, Multilayers Perceptrons, Entropy-based, Hvac System, Hyper-parameter, Meta-heuristics Algorithms, Optimisations, Multi-layer Perceptron, Hyper-parameter Optimizations, Building Hvac Systems
Access URL: https://hdl.handle.net/20.500.12639/7341
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3
Authors: et al.
Source: Cluster Computing. 27(3):3717-3739
Subject Terms: Differential evolution, Gaussian transformation, Hyperparameter optimization, Success history intelligent optimizer, Convolutional neural networks, Deep learning, Evolutionary algorithms, Optimal systems, Optimization, Convolutional neural network, Hyper-parameter, Hyper-parameter optimizations, Near-optimal solutions, Optimization problems, Optimizers, Transformation algorithm, Gaussian distribution
File Description: print
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Authors:
Source: Chips, Vol 2, Iss 2, Pp 130-141 (2023)
Chips; Volume 2; Issue 2; Pages: 130-141Subject Terms: resource constraints, neural architecture search, tiny devices, tiny machine learning, micro controllers, hyper parameter optimizations, on device learning, Electronic computers. Computer science, 0202 electrical engineering, electronic engineering, information engineering, QA75.5-76.95, 02 engineering and technology, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4
File Description: application/pdf
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5
Authors:
Source: 2021 6th International Conference on Computer Science and Engineering (UBMK). :354-358
Subject Terms: Optimization, Nearest-neighbour, Hyperparameter Optimization, Support vector machines, Disease diagnosis, Classification (of information), Decision trees, Optimization algorithms, Classification algorithm, Performances evaluation, Machine Learning, Computer aided diagnosis, Sentiment analysis, Nearest neighbor search, Supervised Learning, Classification Algorithms, Data mining, Machine-learning, Optimisations, Hyper-parameter optimizations, Supervised learning
Access URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=9559003
http://xplorestaging.ieee.org/ielx7/9558874/9558875/09559003.pdf?arnumber=9559003
https://hdl.handle.net/11454/79696
https://doi.org/10.1109/UBMK52708.2021.9559003
https://avesis.atauni.edu.tr/publication/details/fd322c88-3ae5-4131-b8b9-172e3ef18590/oai -
6
Authors:
Contributors:
Source: IEEE Antennas and Wireless Propagation Letters. 21:725-729
Subject Terms: Optimization, Testing, 0211 other engineering and technologies, forecasting, Directional patterns (antenna), 02 engineering and technology, Deep neural network, Antenna measurements, antenna, deep neural network (DNN), Stochastic methods, Stochastic processes, Frequency response, Deep neural networks, Long short-term memory, 0202 electrical engineering, electronic engineering, information engineering, Optimal hyperparameter, Optimization method, Training, Antenna arrays, Hyper-parameter, Hyper-parameter optimizations, Antennas measurement, Stochastic systems, Antenna, long short-term memory (LSTM), optimal hyperparameter, stochastic methods, Brain, Random processes, Antenna modelling, Stochastic models, Slot antennas, Antennas, Optimisations, Forecasting
File Description: application/pdf
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7
Authors: et al.
Contributors: et al.
Source: Scopus
Subject Terms: Decision trees, Forecasting, Learning algorithms, Machine learning, Parameterization, Risk assessment, Safety factor, Soil liquefaction, Soils, Analytical approach, Artificial intelligence learning, Artificial neural network approach, Engineering problems, Factors of safeties, Hyper-parameter optimizations, Machine learning algorithms, Machine-learning, Parameterized, Performance, artificial neural network, liquefaction, optimization, prediction, soil dynamics, Neural networks
Relation: Engineering Geology; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; Q1; 319; Kurnaz, T. F., Erden, C., Kökçam, A. H., Dağdeviren, U., & Demir, A. S. (2023). A hyper parameterized artificial neural network approach for prediction of the factor of safety against liquefaction. Engineering Geology, 319 doi:10.1016/j.enggeo.2023.107109; https://hdl.handle.net/20.500.14002/2011
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8
Click prediction boosting via Bayesian hyperparameter optimization-based ensemble learning pipelines
Authors:
Subject Terms: Bayesian optimization, Ensemble learning, Machine learning, Meta-regressors, Meta-search engines, Online travel agencies, Adaptive boosting, E-learning, Learning systems, Search engines, Bayesian, Hyper-parameter optimizations, Level-1, Machine-learning, Meta search engines, Meta-regressor, Online travel agency, Travel agency, Forecasting
File Description: application/pdf
Relation: Intelligent Systems with Applications; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; https://doi.org/10.1016/j.iswa.2023.200185; https://hdl.handle.net/20.500.12469/4898; 17
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9
Authors: et al.
Contributors: et al.
Source: Chem Sci
Subject Terms: 0301 basic medicine, Design, Ligand, Discovery, Drug interactions, Docking, 03 medical and health sciences, Renin, Deep neural networks, Physical interactions, Computational approach, Drug-target interactions, Structural representation, Hyper-parameter optimizations, In-silico screening, 0303 health sciences, Multidisciplinary, Ligands | Docking | Structure-based virtual, Inhibitors, 4. Education, Screening procedures, Proteins, Convolution, 3. Good health, Benchmarking, Chemistry, Potent, Structural compounds, Cladribine, Convolutional neural networks, Derivatives, Forecasting
File Description: application/pdf
Access URL: https://pubs.rsc.org/en/content/articlepdf/2020/sc/c9sc03414e
https://pubmed.ncbi.nlm.nih.gov/33209251
http://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc03414e
https://pubmed.ncbi.nlm.nih.gov/33209251/
https://pubs.rsc.org/en/content/articlepdf/2020/SC/C9SC03414E
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643205
https://europepmc.org/abstract/PPR/PPR64300
https://pubs.rsc.org/en/content/articlelanding/2020/sc/c9sc03414e
https://www.biorxiv.org/content/10.1101/491365v1
https://www.biorxiv.org/content/biorxiv/early/2018/12/09/491365.full.pdf
https://www.biorxiv.org/content/early/2018/12/09/491365
https://hdl.handle.net/20.500.12508/1127
https://aperta.ulakbim.gov.tr/record/10693 -
10
Authors:
Source: IEEE Control Systems Letters. 1(2):388-393
Subject Terms: Nonlinear systems identification, Optimization, Bayesian networks, Global optimization, Maximum principle, Nonlinear systems, Numerical methods, Bayesian approaches, Covariance structures, Expectation Maximization, Hyper-parameter optimizations, Marginal likelihood, Optimization problems, Regularized least squares, Maximum likelihood estimation
File Description: print
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11
Authors: et al.
Contributors: et al.
Subject Terms: Chemistry, Multidisciplinary, Ligands | Docking | Structure-based virtual, Discovery, Ligand, Inhibitors, Cladribine, Design, Renin, Derivatives, Docking, Potent, Benchmarking, Convolution, Convolutional neural networks, Deep neural networks, Forecasting, Proteins, Computational approach, Drug-target interactions, Hyper-parameter optimizations, In-silico screening, Physical interactions, Screening procedures, Structural compounds, Structural representation, Drug interactions
File Description: application/pdf
Relation: Chemical Science; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; Web of Science Core Collection - Science Citation Index Expanded; Web of Science - Scopus - PubMed; https://doi.org/10.1039/c9sc03414e; https://hdl.handle.net/20.500.12508/1127; 11; 2531; 2557
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12
Authors: et al.
Subject Terms: Energy efficiency, Geothermal Power Plant, Machine Learning, Thermoelectric Generator, Waste Heat Recovery, Decision Trees, Forecasting, Geothermal Fields, Geothermal Power Plants, Hybrid Systems, Learning Algorithms, Learning Systems, Thermal Conductivity, Thermal Energy, Thermoelectric Equipment, Waste Heat, Waste Heat Utilization, Water Levels, Energy Productions, Error Metrices, Hyper-Parameter Optimizations, Intelligence Models, Intelligent Models, Machine-learning, Power- generations, Thermoelectric, Thermoelectric Generators, Waste-heat Recovery, Alternative Energy, Artificial Intelligence
File Description: application/pdf
Relation: Geothermics; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; https://doi.org/10.1016/j.geothermics.2023.102911; https://hdl.handle.net/11492/8053; 118
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13
Authors: et al.
Source: Advances in Intelligent Systems and Computing. :419-431
Subject Terms: 3D light sheet, Bacterial image classification, Convolutional neural network, Deep learning, Feature extraction, Image classification, Bacteria, Big data, Convolution, Convolutional neural networks, Decision trees, Deep neural networks, Fluorescence, Fluorescence microscopy, Network architecture, Random forests, Biological microscopy, Classification accuracy, Experimental analysis, Fluorescence microscopy images, Hyper-parameter optimizations, Image processing technique, Machine learning approaches, Support vector classifiers
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14
Authors:
Subject Terms: Artificial bee colony algorithm, Deep convolution neural network, Hyperparameter optimization, Image identification, Image processing, Arts computing, Classification (of information), Convolution, Deep neural networks, Evolutionary algorithms, Image classification, Image enhancement, Image segmentation, Optimization, Statistical tests, Artificial bees, Bee colony algorithms, Convolution neural network, Convolutional neural network, Hyper-parameter optimizations, Images processing, Neural networks classifiers, Convolutional neural networks
Relation: Journal of Ambient Intelligence and Humanized Computing; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; https://hdl.handle.net/11492/8039; 14; 8827; 8838
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15
Authors: et al.
Source: Proc. Mach. Learn. Res. Proceedings of Machine Learning Research.
Subject Terms: Chemical activation, Image enhancement, Machine learning, Activation functions, Condition, Energy, Fine tuning, Hyper-parameter, Hyper-parameter optimizations, Performance, Pruning techniques, Sparse network, Sparse neural networks, Drops
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16
Authors:
Source: Computer Vision Faculty Publications
Subject Terms: Coronavirus, Polymerase chain reaction, 'current, Binary classification, Chest x-rays, Coronaviruses, COVID-19, Hyper-parameter, Hyper-parameter optimizations, Standards for testing, Health insurance, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Machine Learning (cs.LG), Artificial Intelligence and Robotics, Biomedical Engineering and Bioengineering, Computer Sciences, Medicine and Health Sciences
Time: 20th century
File Description: application/pdf
Relation: https://dclibrary.mbzuai.ac.ae/cvfp/121; https://dclibrary.mbzuai.ac.ae/context/cvfp/article/1120/viewcontent/Hyperparameter_Optimization_for_COVID_19_Chest_X_Ray_Classification_ccby.pdf
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17
Authors: et al.
Source: Proceedings of the 9th ACM Multimedia Systems Conference, MMSys 2018 ; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050695846&doi=10.1145%2f3204949.3208131&partnerID=40&md5=4f65a31225a354866daf7fc893daf4c0
Subject Terms: Brain, Deep learning, Deep neural networks, Multimedia systems, Optimization, Convolutional Neural Networks (CNN), Hyper-parameter optimizations, Manual Search, Neural network (nn), Open source frameworks, Random searches, Recurrent neural network (RNN), Short term memory, Long short-term memory, Association for Computing Machinery, Inc
Relation: http://hdl.handle.net/11615/75360
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18
Authors: et al.
Index Terms: Chemical activation, Image enhancement, Machine learning, Activation functions, Condition, Energy, Fine tuning, Hyper-parameter, Hyper-parameter optimizations, Performance, Pruning techniques, Sparse network, Sparse neural networks, Drops, Computer and Information Sciences, Data- och informationsvetenskap, Conference paper, info:eu-repo/semantics/conferenceObject, text
URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-66091
Proceedings of Machine Learning Research, 26403498
Proc. Mach. Learn. Res.
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