Results 41 to 50 of about 5,001,817 (246)

Metalearning for Hyperparameter Optimization [PDF]

, 2022
SummaryThis chapter describes various approaches for the hyperparameter optimization (HPO) and combined algorithm selection and hyperparameter optimization problems (CASH). It starts by presenting some basic hyperparameter optimization methods, including grid search, random search, racing strategies, successive halving and hyperband. Next, it discusses
Brazdil, Pavel, van Rijn, Jan N., Soares, Carlos, Vanschoren, Joaquin   +3 more
openaire   +2 more sources

Improving classification accuracy of fine-tuned CNN models: Impact of hyperparameter optimization

Heliyon
The 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, Zaneta Swiderska-Chadaj, Krzysztof Siwek, Arkadiusz Gertych   +3 more
doaj   +1 more source

Immunocomputing-Based Approach for Optimizing the Topologies of LSTM Networks

IEEE Access, 2021
This paper aims to automatically design optimal LSTM topologies using the clonal selection algorithm (CSA) to solve text classification tasks such as sentiment analysis and SMS spam classification.
Ali Al Bataineh, Devinder Kaur
doaj   +1 more source

A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification

Journal of Imaging
Breast cancer is considered one of the most-common types of cancers among females in the world, with a high mortality rate. Medical imaging is still one of the most-reliable tools to detect breast cancer.
Khadija Aguerchi, Y. Jabrane, Maryam Habba, Amir Hajjam El Hassani   +3 more
semanticscholar   +1 more source

Squirrel: A Switching Hyperparameter Optimizer

CoRR, 2020
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge. Motivated by the fact that different optimizers work well on different problems, our approach switches between different optimizers. Since the team names on the competition's leaderboard were randomly generated "alliteration nicknames", consisting of an adjective and an ...
Noor H. Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer 0001, Katharina Eggensperger, André Biedenkapp, Diederick Vermetten, Hao Wang 0025, Carola Doerr, Marius Lindauer, Frank Hutter   +11 more
openaire   +3 more sources

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Scientific Reports
Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment.
Umesh Kumar Lilhore, Sarita Simaiya, Yogesh Kumar Sharma, K. Kaswan, K. B. V. B. Rao, V. Rao, Anupam Baliyan, Anchit Bijalwan, Roobaea Alroobaea   +8 more
semanticscholar   +1 more source

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

IEEE Access
Data privacy is essential in the financial sector to protect client’s sensitive information, prevent financial fraud, ensure regulatory compliance, and safeguard intellectual property.
Sandeep Dasari, Rajesh Kaluri
semanticscholar   +1 more source

Theoretical Aspects in Penalty Hyperparameters Optimization

Mediterranean Journal of Mathematics, 2023
AbstractLearning processes play an important role in enhancing understanding and analyzing real phenomena. Most of these methodologies revolve around solving penalized optimization problems. A significant challenge arises in the choice of the penalty hyperparameter, which is typically user-specified or determined through Grid search approaches.
Esposito F., Selicato L., Sportelli C.
openaire   +5 more sources

Overtuning in Hyperparameter Optimization

CoRR
Accepted at the Fourth Conference on Automated Machine Learning (Methods Track).
Lennart Schneider, Bernd Bischl, Matthias Feurer 0001   +2 more
openaire   +4 more sources

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, Cheng Huang, Liangliang Duan   +2 more
doaj   +1 more source