Results 31 to 40 of about 131,851 (273)
Impact of Hyperparameter Optimization on Cross-Version Defect Prediction: An Empirical Study [PDF]
Jisuanji kexue yu tansuo, 2023 In the field of machine learning, hyperparameters are one of the key factors that affect prediction performance. Previous studies have shown that optimizing hyperparameters can improve the performance of inner-version defect prediction and cross-project ...
HAN Hui, YU Qiao, ZHU Yi
doaj +1 more source
A Comparison of AutoML Hyperparameter Optimization Tools For Tabular Data
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 The performance of machine learning (ML) methods for classification and regression tasks applied to tabular datasets is sensitive to hyperparameters values.
Prativa Pokhrel, Alina Lazar
doaj +1 more source
Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
Applied Sciences, 2022 Recommendation attack attempts to bias the recommendation results of collaborative recommender systems by injecting malicious ratings into the rating database. A lot of methods have been proposed for detecting such attacks.
Quanqiang Zhou +2 more
doaj +1 more source
Rectangularization of Gaussian process regression for optimization of hyperparameters
Machine Learning with Applications, 2023 Gaussian process regression (GPR) is a powerful machine learning method which has recently enjoyed wider use, in particular in physical sciences. In its original formulation, GPR uses a square matrix of covariances among training data and can be viewed ...
Sergei Manzhos, Manabu Ihara
doaj +1 more source
Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization [PDF]
E3S Web of Conferences, 2023 There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations.
Muliawan Nicholas Hans +2 more
doaj +1 more source
Optimization of Annealed Importance Sampling Hyperparameters
, 2023 AbstractAnnealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. Although AIS is guaranteed to provide unbiased estimate for any set of hyperparameters, the common implementations rely on simple heuristics such as the geometric average bridging distributions between ...
Shirin Goshtasbpour, Fernando Perez-Cruz
openaire +3 more sources
Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization
HeliyonThe immense popularity of convolutional neural network (CNN) models has sparked a growing interest in optimizing their hyperparameters. Discovering the ideal values for hyperparameters to achieve optimal CNN training is a complex and time-consuming task,
Mikolaj Wojciuk +3 more
doaj +1 more source
Journal of Marine Science and Engineering, 2021 Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based ...
Zhihui Hu +5 more
doaj +1 more source
Hyperparameter Optimization for Portfolio Selection [PDF]
The Journal of Financial Data Science, 2020 Portfolio selection involves a trade-off between maximizing expected return and minimizing risk. In practice, useful formulations also include various costs and constraints that regularize the problem and reduce the risk due to estimation errors, resulting in solutions that depend on a number of hyperparameters.
Nystrup, Peter +2 more
openaire +2 more sources
Efficient Optimization of Echo State Networks for Time Series Datasets
, 2019 Echo State Networks (ESNs) are recurrent neural networks that only train their output layer, thereby precluding the need to backpropagate gradients through time, which leads to significant computational gains.
Gianniotis, Nikos +2 more
core +1 more source