Results 11 to 20 of about 211,846 (266)

Hyperparameter Importance Across Datasets [PDF]

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018
With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond ...
Bergstra J., Bonilla E. V., Brazdil P., Demvsar J., Feurer M., Jamieson K., Joachims T., Klein A., Li L., Loshchilov I., Sobol I. M., van Rijn J. N., van Rijn J. N., Wistuba M.   +13 more
core   +2 more sources

Hyperparameter Optimization [PDF]

, 2019
Recent interest in complex and computationally expensive machine learning models with many hyperparameters, such as automated machine learning (AutoML) frameworks and deep neural networks, has resulted in a resurgence of research on hyperparameter optimization (HPO). In this chapter, we give an overview of the most prominent approaches for HPO.
Feurer, Matthias, Hutter, Frank
openaire   +2 more sources

AutoRL Hyperparameter Landscapes

International Conference on AutoML, 2023
Although Reinforcement Learning (RL) has shown to be capable of producing impressive results, its use is limited by the impact of its hyperparameters on performance. This often makes it difficult to achieve good results in practice. Automated RL (AutoRL) addresses this difficulty, yet little is known about the dynamics of the hyperparameter landscapes ...
Mohan, Aditya, Benjamins, Carolin, Wienecke, Konrad, Dockhorn, Alexander, Lindauer, Marius   +4 more
openaire   +2 more sources

Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan

Applied Sciences, 2022
Deep learning networks (DLNs) use multilayer neural networks for multiclass classification that exhibit better results in wind-power forecasting applications.
Wen-Hui Lin, Ping Wang, Kuo-Ming Chao, Hsiao-Chung Lin, Zong-Yu Yang, Yu-Huang Lai   +5 more
doaj   +1 more source

Comparative Analysis of Neural Networking and Regression Models for Time Series Forecasting

Цифровая трансформация, 2019
Applicability of neural nets in time series forecasting has been considered and researched. For this, training of neural network on various time series with preliminary selection of optimal hyperparameters has been performed.
S. V. Sholtanyuk
doaj   +1 more source

Multi-Classification of Motor Imagery EEG Signals Using Bayesian Optimization-Based Average Ensemble Approach

Applied Sciences, 2022
Motor Imagery (MI) classification using electroencephalography (EEG) has been extensively applied in healthcare scenarios for rehabilitation aims.
Souha Kamhi, Shuai Zhang, Mohamed Ait Amou, Mohamed Mouhafid, Imran Javaid, Isah Salim Ahmad, Isselmou Abd El Kader, Ummay Kulsum   +7 more
doaj   +1 more source

Combining cosmological datasets: hyperparameters and Bayesian evidence [PDF]

, 2002
A method is presented for performing joint analyses of cosmological datasets, in which the weight assigned to each dataset is determined directly by it own statistical properties.
Bishop, Bond, Bridle, Bridle, De Bernardis, Erdogdu, Godwin, Halverson, Jaffe, Jaynes, Jaynes, John, Knox, Lahav, Lee, Lewis, M. P. Hobson, Netterfield, O Lahav, Press, S. L. Bridle, Shannon, Sivia   +22 more
core   +3 more sources

Learning the Structure for Structured Sparsity [PDF]

, 2015
Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings.
Bach, Francis, Shervashidze, Nino
core   +6 more sources

Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion

Entropy, 2023
Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
doaj   +1 more source

On Hyperparameter Optimization of Machine Learning Methods Using a Bayesian Optimization Algorithm to Predict Work Travel Mode Choice

IEEE Access, 2023
Prediction of work Travel mode choice is one of the most important parts of travel demand forecasting. Planners can achieve sustainability goals by accurately forecasting how people will get to and from work.
Mahdi Aghaabbasi, Mujahid Ali, Michal Jasinski, Zbigniew Leonowicz, Tomas Novak   +4 more
doaj   +1 more source