Results 61 to 70 of about 200,311 (278)
Proximal boosting and its acceleration
, 2020 Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model. From an optimization point of view, the learning procedure of gradient boosting mimics a gradient descent on a functional variable ...
Boyer, Claire +2 more
core
Advanced Functional Materials, EarlyView.In this work, a magnetic core‐shell catalyst (HOF‐on‐Fe3O4/ZIF‐67) is successfully synthesized, consisting of a metal–organic framework (ZIF‐67) with magnetic Fe3O4 as the core and a porous hydrogen‐bonded organic framework (HOF) as the shell. The catalyst efficiently activated peroxymonosulfate, resulting in rapid and effective removal of water ...
Yingying Du +4 more
wiley +1 more source
Machine Learning Approaches for Fatigue Life Prediction of Steel and Feature Importance Analyses
InfrastructuresPredicting fatigue behavior in steel components is highly challenging due to the nonlinear and uncertain nature of material degradation under cyclic loading.
Babak Naeim +6 more
doaj +1 more source
, 2015 We extend the theory of boosting for regression problems to the online learning setting. Generalizing from the batch setting for boosting, the notion of a weak learning algorithm is modeled as an online learning algorithm with linear loss functions that competes with a base class of regression functions, while a strong learning algorithm is an online ...
Beygelzimer, Alina +3 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.The layer‐by‐layer (LbL) assembly of coordination solids, enabled by the surface‐mounted metal‐organic framework (SURMOF) platform, is on the cusp of generating the organic counterpart of the epitaxy of inorganics. The programmable and sequential SURMOF protocol, optimized by machine learning (ML), is suited for accessing high‐quality thin films of ...
Zhengtao Xu +2 more
wiley +1 more source
Biomass Native Structure Into Functional Carbon‐Based Catalysts for Fenton‐Like Reactions
Advanced Functional Materials, EarlyView.This study indicates that eight biomasses with 2D flaky and 1D acicular structures influence surface O types, morphology, defects, N doping, sp2 C, and Co nanoparticles loading in three series of carbon, N‐doped carbon, and cobalt/graphitic carbon. This work identifies how these structural factors impact catalytic pathways, enhancing selective electron
Wenjie Tian +7 more
wiley +1 more source
Frontiers in Public Health, 2022 BackgroundIn this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery.MethodsWe used 10 machine learning and deep learning algorithms to establish a ...
Cheng-Mao Zhou +7 more
doaj +1 more source
An Evaluation of Classification and Outlier Detection Algorithms [PDF]
, 2018 This paper evaluates algorithms for classification and outlier detection accuracies in temporal data. We focus on algorithms that train and classify rapidly and can be used for systems that need to incorporate new data regularly.
Austin, Jim, Hodge, Victoria J.
core +1 more source
Electroactive Metal–Organic Frameworks for Electrocatalysis
Advanced Functional Materials, EarlyView.Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska +7 more
wiley +1 more source
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
, 2017 We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule classification focusing on (i) usefulness of gradient tree boosting (XGBoost) and (ii) effectiveness of parameter optimization using Bayesian optimization (Tree Parzen Estimator,
Kojima, Ryosuke +6 more
core +2 more sources