Results 91 to 100 of about 1,043 (255)
On the ordinary ridge regression estimator based on a fixed biasing constant.
, 1989 Since the seminal work of Hoerl and Kennard (1970a), ridge regression has proven to be a useful technique to tackle the multicollinearity problems in the linear regression model, and much research on this topic has been published. A strange phenomenon in
An, Sang-Sin
core
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary +1 more
wiley +1 more source
Predictive efficiency of ridge regression estimator
, 2017 In this article we have considered the problem of prediction within and outside the sample for actual and average values of the study variables in case of ordinary least squares and ridge regression estimators.
Sharma Amit, Tiwari Manoj
core +2 more sources
Advanced Intelligent Systems, EarlyView.Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source
Scientific AfricanThe linear regression model is a widely used statistical tool that forms most modelling concepts' basis. The ordinary least square estimator is often adopted to estimate the model's parameters.
Segun L. Jegede +6 more
doaj +1 more source
Ag/Ag2S Nanoparticle‐Based In‐Materio Lightweight Cryptographic System for IoT Edge Security
Advanced Intelligent Systems, EarlyView.This work presents a nanomaterial‐based in materio encryption method that directly transforms analog signals through nonlinear Ag/Ag2S nanoparticle networks. By exploiting the inherently nonuniform characteristics that arise from random arrangement of nanoparticles as a unique security key, the approach produces highly complex encrypted waveforms ...
Hiroki Tabata +7 more
wiley +1 more source
A Poisson Ridge Regression Estimator
The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML). The ML method is very sensitive to multicollinearity. Therefore, we present a new Poisson ridge regression
Shukur, Ghazi, Månsson, Kristofer
core
Time‐Delayed Spiking Reservoir Computing Enables Efficient Time Series Prediction
Advanced Intelligent Systems, EarlyView.This study proposes time‐delayed spiking reservoir computing (TDSRC) for efficient time series prediction. By concatenating time‐lagged states, TDSRC constructs an expanded readout feature vector without altering internal reservoir dynamics. This approach enables highly accurate forecasting with significantly fewer neurons, providing a resource ...
Pin Jin +3 more
wiley +1 more source
Improving the Ordinary Least Squares Estimator by Ridge Regression
OALib, 2022 openaire +1 more source