Results 201 to 210 of about 5,001,817 (246)

Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition

International Symposium on Advanced Topics in Electrical Engineering, 2021
This paper presents a method of optimizing the hyperparameters of a convolutional neural network in order to increase accuracy in the context of facial emotion recognition.
A. Vulpe-Grigorași, O. Grigore
semanticscholar   +1 more source

A survey on hyperparameters optimization algorithms of forecasting models in smart grid

Sustainable cities and society, 2020
Forecasting in the smart grid (SG) plays a vital role in maintaining the balance between demand and supply of electricity, efficient energy management, better planning of energy generation units and renewable energy sources and their dispatching and ...
Rabiya Khalid, N. Javaid
semanticscholar   +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
openaire   +1 more source

Metaheuristic optimization of data preparation and machine learning hyperparameters for prediction of dynamic methane production.

Bioresource Technology, 2023
Machine learning algorithms provide detailed description of the anaerobic digestion process, but the impact of data preparation procedures and hyperparameter optimization has rarely been investigated. A genetic algorithm was developed for optimizing data
Alberto Meola, M. Winkler, S. Weinrich
semanticscholar   +1 more source

Efficient Hyperparameters optimization Through Model-based Reinforcement Learning and Meta-Learning

2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), 2020
Hyperparameter optimization (HPO) plays a vital role in the performance of machine learning algorithms. When the algorithm is complex or the dataset is large, the computational cost of algorithm evaluation is very high, which is a major challenge for HPO.
Xiyuan Liu, Jia Wu, Senpeng Chen
semanticscholar   +1 more source

Improving grasshopper optimization algorithm for hyperparameters estimation and feature selection in support vector regression

, 2021
High-dimensionality is one of the major problems which affect the quality of the classification and prediction modeling. Support vector regression has been applied in several real problems.
Z. Algamal, M. Qasim, Muhammad Hisyam Lee, Haithem Taha Mohammad Ali   +3 more
semanticscholar   +1 more source

Learning hyperparameter optimization initializations

2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015
Hyperparameter optimization is often done manually or by using a grid search. However, recent research has shown that automatic optimization techniques are able to accelerate this optimization process and find hyperparameter configurations that lead to better models.
Martin Wistuba, Nicolas Schilling, Lars Schmidt-Thieme   +2 more
openaire   +2 more sources

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

Hyperparameter optimization in learning systems

Journal of Membrane Computing, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +3 more sources

Optimization on selecting XGBoost hyperparameters using meta‐learning

Expert Syst. J. Knowl. Eng.
With computational evolution, there has been a growth in the number of machine learning algorithms and they became more complex and robust. A greater challenge is upon faster and more practical ways to find hyperparameters that will set up each algorithm
T. L. Marinho, Diego Carvalho do Nascimento, Bruno A. Pimentel   +2 more
semanticscholar   +1 more source