Results 111 to 120 of about 47,583 (209)
, 2021 Neural networks (NNs) have already been widely used in many research fields. In contrast to traditional mathematical models or machine learning strategies, NN is able to enhance the prediction accuracy.
Zhuang, Zhang-Han
core
Kernel matrix approximation for learning the kernel hyperparameters
, 2012 International audienceThe selection of kernel hyperparameters is addressed in this article. The proposed method is based on the approximation of an empirical ideal kernel matrix using three measures of similarity between matrices. The conventional kernel
Mathieu Fauvel, Fauvel, Mathieu
core +1 more source
On Optimizing Hyperparameters for Quantum Neural Networks
2024 IEEE International Conference on Quantum Computing and Engineering (QCE)The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training. Therefore, training is usually outsourced into HPC facilities, where we have started to experience limits in scaling conventional HPC hardware, as theorized by Moore's law.
Sabrina Herbst +2 more
openaire +2 more sources
Beyond Manual Tuning of Hyperparameters
, 2015 The success of hand-crafted machine learning systems in many applications raises the question of making machine learning algorithms more autonomous, i.e., to reduce the requirement of expert input to a minimum. We discuss two strategies towards this goal:
Hutter, Frank +2 more
core
Scientific ReportsGiven that the decision tree C4.5 algorithm has outstanding performance in prediction accuracy on medical datasets and is highly interpretable, this paper carries out an optimization study on the selection of hyperparameters of the algorithm in order to ...
Yiyan Zhang, Yi Xin, Qin Li
doaj +1 more source
Automatic hyperparameter tuning of topology optimization algorithms using surrogate optimization
This paper presents a new approach that automates the tuning process in topology optimization of parameters that are traditionally defined by the user. The new method draws inspiration from hyperparameter optimization in machine learning.
Ha, Dat, Carstensen, Josephine
core +1 more source
Understanding weight-magnitude hyperparameters in training binary networks
, 2022 Binary Neural Networks (BNNs) are compact and efficient by using binary weights instead of real-valued weights. Current BNNs use latent real-valued weights during training, where several training hyper-parameters are inherited from real-valued networks ...
Quist, Joris (author)
core
Sequential Optimization of Decision Feedback Equalizer Hyperparameters in Mobile Acoustic Channels
International audienceMobile underwater acoustic communication (UAC) is affected by fast-changing propagation conditions, posing serious challenges to maintaining reliable data transmission.
Socheleau, François-Xavier +2 more
core +1 more source
Multi-Objective Hyperparameter Optimization in Machine Learning—An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning (ML) workflows. This arises from the fact that ML methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly
Karl, Florian M. +12 more
core +1 more source
Retraction notice to "Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques" [Heliyon 10 (2024) e26192]. [PDF]
HeliyonLi L +10 more
europepmc +1 more source