Results 71 to 80 of about 1,862,731 (241)
Robust optimization of SVM hyper-parameters for spillway type selection
Ain Shams Engineering Journal, 2021 Spillways, which play a vital role in dams, can be built in various types. Although several studies have been conducted on hydraulic calculations of spillways, studies on type selection that require heuristics knowledge were limited.
Enes Gul, Nuh Alpaslan, M. Emin Emiroglu
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Tuning Bayesian optimization for materials synthesis: simulating two- and three-dimensional cases
Science and Technology of Advanced Materials: Methods, 2023 Compared to the optimization of a 1D synthesis parameter in materials synthesis, the optimization of multi-dimensional synthesis parameters is challenging for researchers.
Han Xu +8 more
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Frugal Optimization for Cost-related Hyperparameters
, 2021 The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost.
Wang, Chi, Huang, Silu, Wu, Qingyun
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Sentiment Analysis on Twitter Using Deep Belief Network Optimized with Particle Swarm Optimization [PDF]
E3S Web of ConferencesDeep Belief Network is a type of artificial neural network that is widely used in machine learning and deep learning tasks that allows it to learn hierarchical representations of the input data.
Dewi Irma Amelia +1 more
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Design of adaptive soft sensor based on Bayesian optimization
Case Studies in Chemical and Environmental Engineering, 2022 When adaptive soft sensors are introduced to industrial plants, an appropriate combination of the type of adaptation mechanism, hyperparameters of the mechanism, regression model, and hyperparameters of the model must be selected for predictive soft ...
Shuto Yamakage, Hiromasa Kaneko
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Optimizing Hyperparameters with Conformal Quantile Regression
CoRR, 2023 Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capture uncertainty but they make strong assumptions about the observation noise, which might not be ...
David Salinas +4 more
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An Effective Hyperparameters Optimization Algorithm for Metro Passenger Flow Prediction
, 2021 An accurate neural network model must rely on suitable model architecture and an appropriate model training process. The hyperparameters optimization problem aims to search for the best neural network architecture in a large solution space and maximize ...
Fang, Zhi-Yan
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Computer Assisted Methods in Engineering and ScienceThis paper describes the application of particle swarm optimization (PSO) for the hyperparameter optimization problem of multi-layered perceptron (MLP) model.
Kenta Shiomi, Tetsuya Sato, Eisuke Kita
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Optimization-seeking experiment with hyperparameters K.
Optimization-seeking experiment with hyperparameters K.
Xi Zhang (83736) +5 more
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Scientific ReportsLand cover and land use (LCLU) information is crucial in different earth observation applications, such as environmental management, infrastructure planning, and urban development.
Pooya Heidari, Asghar Milan
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