Results 131 to 140 of about 3,187,155 (299)

Robust generalized cross-validation for choosing the regularization parameter [PDF]

, 2006
Let fλ be the regularized solution for the problem of estimating a function or vector f0 from noisy data yi ≤ Lif0 + εi, i ≤ 1, ..., n, where Li are linear functionals.
Lukas, M.A.
core  

Comparative Analysis of Model‐Agnostic Explanation Methods in Materials Science

Advanced Intelligent Systems, EarlyView.
To address the critical lack of explainable artificial intelligence (XAI) benchmarks in materials science, we present a quantitative and qualitative analysis of six XAI methods applied to molecular fingerprints. Our results reveal significant discrepancies in feature importance rankings, demonstrating that the chosen explanation approach introduces ...
Anna Przybyłowska, Joanna Neubauer, Monika Sztuder, Witold Taisner, Agnieszka Hryniewicka, Julia Kolodziejczyk, Marta Eliza Plonska‐Brzezinska, Dariusz Brzezinski   +7 more
wiley   +1 more source

A New Ridge Regression Causality Test in the Presence of Multicollinearity [PDF]

This paper analyzes and compares the properties of the most commonly applied versions of the Granger causality (GC) test to a new ridge regression GC test (RRGC), in the presence of multicollinearity.
Shukur, Ghazi, Månsson, Kristofer, Sjölander, Pär   +2 more
core  

Dynamic Behavioral Profiling for User Authentication Using Binary Pressure Sequences and Grip Patterns via a Soft Stacking Ensemble

Advanced Intelligent Systems, EarlyView.
A smartphone‐embedded fabric‐strip sensing interface captures side‐pressure sequences and grip‐position patterns for behavioral authentication. Probability‐based soft stacking integrates class‐wise predictive probabilities from multiple learners to improve verification‐style discrimination, supporting a sensor‐integrated mobile‐security framework that ...
Wonki Hong
wiley   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

High-dimensional Asymptotics of Generalization Performance in Continual Ridge Regression

CoRR
Continual learning is motivated by the need to adapt to real-world dynamics in tasks and data distribution while mitigating catastrophic forgetting. Despite significant advances in continual learning techniques, the theoretical understanding of their generalization performance lags behind.
Yihan Zhao, Wenqing Su, Ying Yang
openaire   +3 more sources

(Non) Linear Regression Modeling [PDF]

We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1, . . .
Čížek, Pavel
core  

Preferential trading in agriculture: New insights from a structural gravity analysis and machine learning

American Journal of Agricultural Economics, EarlyView.
Abstract Preferential trade agreements (PTAs) contain various non‐tariff provisions, yet identifying their trade effects remains challenging because these commitments are high‐dimensional and strongly correlated within agreements. We estimated a theory‐consistent structural gravity model with domestic flows for 26 agricultural subsectors over 1988–2017
Dongin Kim, Sandro Steinbach
wiley   +1 more source

Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods

Health and Quality of Life Outcomes
Background Preference-based measures of health-related quality of life (HRQoL), such as the Short Form Six-Dimension (SF-6D) is essential for health economic evaluations.
Longlin Jiang, Kexun Li, Simiao Lu, Zhou Hong, Yifang Wang, Qin Xie, Qin He, Sirui Wei, Aoru Zhou, Hong Kang, Xuefeng Leng, Qing Yang, Yan Miao   +12 more
doaj   +1 more source

High‐elevation endemic plants predicted to lose habitat from changing climate in Washington State

American Journal of Botany, EarlyView.
Abstract Premise High‐elevation plants face unique challenges from potential climate change impacts that will likely require upslope migration into increasingly smaller suitable habitat. This situation is particularly acute for endemic species that by definition occupy small geographic ranges.
Nicholas L. Gjording, Erik W. Ertsgaard, David E. Giblin, Iris H. Dimpsey, Owen B. Bard   +4 more
wiley   +1 more source