Results 51 to 60 of about 178,006 (285)

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, Alouini, Mohamed-Slim, Elkhalil, Khalil, Kammoun, Abla, Zhang, Xiangliang   +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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +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

Single‐ and Dual‐Atom Configurations in Atomically Dispersed Catalysts for Lithium–Sulfur Batteries

Advanced Functional Materials, EarlyView.
Single‐atom and dual‐atom‐based atomically dispersed catalysts (ADCs) effectively address the shuttle effect and sluggish redox kinetics in Li–S batteries. With nearly 100% atomic utilization and tunable coordination environments, ADCs enhance LiPSs adsorption, lower conversion barriers, and accelerate sulfur redox reactions.
Haoyang Xu, Zhenfeng Li, Yue Fei, Ali Mohammadi, Ge Li   +4 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, De Baets, Bernard, Pahikkala, Tapio, Stock, Michiel, Waegeman, Willem   +4 more
core   +1 more source

Structure Formation in Butterfly Scales: Interplay of Genetic Control, Mechanical Instabilities, and Dynamic Material Properties

Advanced Functional Materials, EarlyView.
Butterfly wing scales are intricate cuticular functional nanosctructures. This perspective suggests that spatially varying material properties, cytoskeletal constraints, and growth‐driven mechanical instabilities shape the resulting nanoscale architectures created from single cells.
Anupama Prakash, Victoria Lloyd, Yi Yang, Anna‐Lee Jessop, Anthony McDougal, Primož Pirih, Guillaume Duclos, Andrew Parnell, Mathias Kolle, Nicola J. Nadeau, Bodo D. Wilts   +10 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

Optoelectronic Control of Redox Dynamics in POM Memristors for Noise‐Resilient Speech and Hardware‐Level Motion Recognition

Advanced Functional Materials, EarlyView.
Optoelectronic control of redox‐active polyoxometalate clusters in polymer matrices yields hybrid memristors with switchable volatile and non‐volatile modes, enabling reservoir‐type in‐sensor optical preprocessing and stable multilevel synapses for multimodal neuromorphic computing, including noise‐tolerant audiovisual keyword recognition and hardware ...
Xiangyu Ma, Ziyu Lv, Guohua Zhang, Zhongyue Xu, Yongbiao Zhai, Haiming Huang, Qiyan Zhang, Xiaojin Zhao, Yan Wang, Ye Zhou, Ziyu Xiong, Guanglong Ding, Wentao Xu, Su‐Ting Han   +13 more
wiley   +1 more source

An adaptive Ridge procedure for L0 regularization [PDF]

, 2015
Penalized selection criteria like AIC or BIC are among the most popular methods for variable selection. Their theoretical properties have been studied intensively and are well understood, but making use of them in case of high-dimensional data is ...
Frommlet, Florian, Nuel, Gregory
core   +6 more sources