Results 71 to 80 of about 1,872,984 (299)
Frugal Optimization for Cost-related Hyperparameters
, 2021 The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost.
Wang, Chi, Huang, Silu, Wu, Qingyun
core +1 more source
New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design
Advanced Engineering Materials, EarlyView.This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare +5 more
wiley +1 more source
Sentiment Analysis on Twitter Using Deep Belief Network Optimized with Particle Swarm Optimization [PDF]
E3S Web of ConferencesDeep Belief Network is a type of artificial neural network that is widely used in machine learning and deep learning tasks that allows it to learn hierarchical representations of the input data.
Dewi Irma Amelia +1 more
doaj +1 more source
Heart disease classification using optimized Machine learning algorithms
Iraqi Journal for Computer Science and Mathematics, 2023 Early detection of heart disease is exceptionally critical to saving the lives of human beings. Heart attack is one of the primary causes of high death rates throughout the world, due to the lack of human and logistical resources in addition to the high
Mohammad Abood Kadhim +1 more
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An Effective Hyperparameters Optimization Algorithm for Metro Passenger Flow Prediction
, 2021 An accurate neural network model must rely on suitable model architecture and an appropriate model training process. The hyperparameters optimization problem aims to search for the best neural network architecture in a large solution space and maximize ...
Fang, Zhi-Yan
core
Advanced Engineering Materials, EarlyView.This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani +7 more
wiley +1 more source
Optimizing Hyperparameters with Conformal Quantile Regression
CoRR, 2023 Many state-of-the-art hyperparameter optimization (HPO) algorithms rely on model-based optimizers that learn surrogate models of the target function to guide the search. Gaussian processes are the de facto surrogate model due to their ability to capture uncertainty but they make strong assumptions about the observation noise, which might not be ...
David Salinas +4 more
openaire +4 more sources
Computer Assisted Methods in Engineering and ScienceThis paper describes the application of particle swarm optimization (PSO) for the hyperparameter optimization problem of multi-layered perceptron (MLP) model.
Kenta Shiomi, Tetsuya Sato, Eisuke Kita
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Optimization-seeking experiment with hyperparameters K.
Optimization-seeking experiment with hyperparameters K.
Xi Zhang (83736) +5 more
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Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning
Advanced Engineering Materials, EarlyView.Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose +7 more
wiley +1 more source