Results 131 to 140 of about 1,355,935 (279)

EXTRACTING RULES FROM TRAINED RBF NEURAL NETWORKS

open access: yesEnvironment. Technology. Resources. Proceedings of the International Scientific and Practical Conference, 2005
This paper describes a method of rule extraction from trained artificial neural networks. The statement of the problem is given. The aim of rule extraction procedure and suitable neural networks for rule extraction are outlined. The RULEX rule extraction algorithm is discussed that is based on the radial basis function (RBF) neural network.
openaire   +3 more sources

Unveiling the factors of aesthetic preferences with explainable AI

open access: yesBritish Journal of Psychology, EarlyView.
Abstract The allure of aesthetic appeal in images captivates our senses, yet the underlying intricacies of aesthetic preferences remain elusive. In this study, we pioneer a novel perspective by utilizing several different machine learning (ML) models that focus on aesthetic attributes known to influence preferences.
Derya Soydaner, Johan Wagemans
wiley   +1 more source

Multimodal machine learning for surgical decision support in epilepsy: Current evidence and translational gaps

open access: yesEpilepsia, EarlyView.
Abstract Objective This systematic review synthesizes evidence on multimodal machine learning (ML) decision support systems for epilepsy surgery focusing on postsurgical outcome prediction, with emphasis on methodological quality and implications for clinical practice.
Mattia Mercier   +2 more
wiley   +1 more source

Machine Learning Model for Predicting Postoperative Pain in Cases of Irreversible Pulpitis

open access: yesInternational Endodontic Journal, EarlyView.
ABSTRACT Aim Postoperative pain is a frequent clinical concern following endodontic treatment. This study aimed to develop and validate supervised machine learning models to predict the occurrence of postoperative pain in cases of irreversible pulpitis.
Pedro Felipe de Jesus Freitas   +9 more
wiley   +1 more source

A Note on Local Polynomial Regression for Time Series in Banach Spaces

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT This work extends local polynomial regression to Banach space‐valued time series for estimating smoothly varying means and their derivatives in non‐stationary data. The asymptotic properties of both the standard and bias‐reduced Jackknife estimators are analyzed under mild moment conditions, establishing their convergence rates.
Florian Heinrichs
wiley   +1 more source

Home - About - Disclaimer - Privacy