Results 51 to 60 of about 177,424 (286)
Risk Convergence of Centered Kernel Ridge Regression with Large Dimensional Data
, 2019 This paper carries out a large dimensional analysis of a variation of kernel ridge regression that we call \emph{centered kernel ridge regression} (CKRR), also known in the literature as kernel ridge regression with offset.
Al-Naffouri, Tareq +4 more
core +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Competing with Gaussian linear experts [PDF]
, 2009 We study the problem of online regression. We prove a theoretical bound on the square loss of Ridge Regression. We do not make any assumptions about input vectors or outcomes.
Vovk, Vladimir, Zhdanov, Fedor
core +2 more sources
Nano‐ and Micro‐Sized Solid Materials Used as Antiviral Agents
Advanced Functional Materials, EarlyView.Due to the rise of viral infections in humans and possible viral outbreaks, the use of nano‐ or micro‐sized materials as antiviral agents is rapidly increasing. This review explores their antiviral properties against RNA and DNA viruses, either as a prevention or a treatment tool, by delving into their mechanisms of action and how to properly assess ...
Orfeas‐Evangelos Plastiras +6 more
wiley +1 more source
Lecture notes on ridge regression
, 2020 The linear regression model cannot be fitted to high-dimensional data, as the high-dimensionality brings about empirical non-identifiability. Penalized regression overcomes this non-identifiability by augmentation of the loss function by a penalty (i.e ...
van Wieringen, Wessel N.
core
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression
, 2018 Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction or network inference problems.
Airola, Antti +4 more
core +1 more source
Advanced Healthcare Materials, EarlyView.Hybrid wrinkled topographies coordinate immune, tissue, and bacterial interactions. The surfaces promote osteointegration, tune macrophage polarization, and inhibit biofilm formation, highlighting a multifunctional strategy for next‐generation implant design.
Mohammad Asadi Tokmedash +4 more
wiley +1 more source
Applications of Some Improved Estimators in Linear Regression [PDF]
, 2005 The problem of estimation of the regression coefficients under multicollinearity situation for the restricted linear model is discussed. Some improve estimators are considered, including the unrestricted ridge regression estimator (URRE), restricted ...
Kibria, B. M. Golam
core +2 more sources
Advanced Healthcare Materials, EarlyView.A compact handheld GelSight probe reconstructs in vivo 3‐D skin topography with micron‐level precision using a custom elastic gel and a learning‐based surface normal to height map pipeline. The device quantifies wrinkle depth across various body locations and detects changes in wrinkle depth following moisturizer application.
Akhil Padmanabha +12 more
wiley +1 more source
Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization [PDF]
, 2013 We introduce a proximal version of the stochastic dual coordinate ascent method and show how to accelerate the method using an inner-outer iteration procedure. We analyze the runtime of the framework and obtain rates that improve state-of-the-art results
Shalev-Shwartz, Shai, Zhang, Tong
core +1 more source