Results 271 to 280 of about 47,079 (313)

LightGBM for Liver Disease Detection with Hybrid Hyperparameter Optimization

Fajar Ratnawati, Agus Tedyyana, Johny Custer   +2 more
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Machine Learning-Based Prediction of Textural Properties and Nonlinear Regulatory Pattern Analysis of 3D-Printed Dough Containing Konjac Glucomannan. [PDF]

Foods
Leng W, Sun Y, Xie J, Pang J.
europepmc   +1 more source

MSQPSO-Optimized MSCC-CAE for Sensor Fault Detection and Localization in Small Modular Reactors. [PDF]

Sensors (Basel)
Zhang W, Wan X, Li X, He Z, Luo M.
europepmc   +1 more source

A Hybrid CNN-GRU-SE Forecasting Method for Short-Term Photovoltaic Power Considers AFD and Data Aggregation. [PDF]

Entropy (Basel)
Liu K, Jia D, Zhan H, Zhou J, Wang Z, Bao J.   +5 more
europepmc   +1 more source

Short-term load forecasting using a two-stage CPO-PSO hyperparameter optimization of LSSVM

XinHao Zhang
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Stochastic Grey Wolf Optimization for Hyperparameter Tuning of LSTM and RNN Models in Energy Forecasting. [PDF]

Sci Rep
Albser OA, Mouhamed MR, Shatta SA, Sweilem NH, Darwish A.   +4 more
europepmc   +1 more source

Hyperparameters and their ranges for optimization in the credit risk prediction method.

Cai Yuanqing (22513351), Zhenming Gao (11789955), Zhang Jian (9250393), Roohallah Alizadehsani (6445298), Paweł Pławiak (17328063)   +4 more
openalex   +2 more sources

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Reproducible Hyperparameter Optimization

Journal of Computational and Graphical Statistics, 2021
A key issue in machine learning research is the lack of reproducibility. We illustrate what role hyperparameter search plays in this problem and how regular hyperparameter search methods can lead t...
Lars Hertel, Pierre Baldi, Daniel L. Gillen   +2 more
openaire   +1 more source

Hyperparameter Optimization Machines

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016
Algorithm selection and hyperparameter tuning are omnipresent problems for researchers and practitioners. Hence, it is not surprising that the efforts in automatizing this process using various meta-learning approaches have been increased. Sequential model-based optimization (SMBO) is ne of the most popular frameworks for finding optimal hyperparameter
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
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Gradient-Based Optimization of Hyperparameters

Neural Computation, 2000
Many machine learning algorithms can be formulated as the minimization of a training criterion that involves a hyperparameter. This hyperparameter is usually chosen by trial and error with a model selection criterion. In this article we present a methodology to optimize several hyper-parameters, based on the computation of the gradient of a model ...
openaire   +2 more sources