Results 201 to 210 of about 72,540 (253)

Friend, Not Foe: Lowered Tissue Reactivity to Long‐Term Polyimide Implants

Advanced Science, EarlyView.
The choice of optimal neural probe designs remains a major challenge in the field of neurotechnology. This study investigated the biocompatibility of several probe variations, including material, thickness, width, and implantation strategy. It highlights the clear advantage of soft polyimide probes over stiff silicon probes for better device ...
Corinne Orlemann, Laura M. De Santis, Paul Neering, Christian Boehler, Kirti Sharma, Arno Aarts, Tobias Holzhammer, Rik J. J. van Daal, Patrick Ruther, Maria Asplund, Roxana N. Kooijmans, Pieter R. Roelfsema   +11 more
wiley   +1 more source

Fundamentals of Thermal Transport and Energy Conversion in Ultra‐High Temperature Ceramics: From Microscale Mechanisms to Macroscopic Properties

Advanced Science, EarlyView.
This review critically examines thermal transport and radiative properties of ultra‐high temperature ceramics for hypersonic flight, advanced nuclear systems, and next‐generation energy conversion devices. It explores phonon–photon–electron interactions, microstructural engineering, thermoelectric conversion, and machine learning‐accelerated multiscale
Zhipeng Pei, Yue Li, Jiayu Xing, Yue Zhu, Ru Zhang, Huan Wu, Zihao Qin, Gang Pei, Man Li   +8 more
wiley   +1 more source

Some of the next articles are maybe not open access.

Related searches:

On the structure of partial least squares regression

Communications in Statistics Part B: Simulation and Computation, 1988
We prove that the two algorithms given in the literature for partial least squares regression are equivalent, and use this equivalence to give an explicit formula for the resulting prediction equation. This in turn is used to investigate the regression method from several points of view. Its relation to principal component regression is clearified, and
Inge Helland
exaly   +2 more sources

Kernel Partial Least-Squares Regression

The 2006 IEEE International Joint Conference on Neural Network Proceedings, 2006
A couple of regularized least squares regression models in a feature space are extended by the kernel partial least squares (KPLS) regression model in this paper. PLS is a method based on the projection of input (explanatory) variables to the latent variables (components), and has been developed and established as one of the multivariate statistical ...
Bai Yifeng, Xiao Jian, Yu Long
openaire   +1 more source

Partial least squares regression for graph mining

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008
Attributed graphs are increasingly more common in many application domains such as chemistry, biology and text processing. A central issue in graph mining is how to collect informative subgraph patterns for a given learning task. We propose an iterative mining method based on partial least squares regression (PLS).
Hiroto Saigo, Nicole Krämer 0002, Koji Tsuda   +2 more
openaire   +2 more sources

Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression

2012
Partial least square (PLS) methods (also sometimes called projection to latent structures) relate the information present in two data tables that collect measurements on the same set of observations. PLS methods proceed by deriving latent variables which are (optimal) linear combinations of the variables of a data table.
Hervé, Abdi, Lynne J, Williams
openaire   +2 more sources

Partial least median of squares regression

Journal of Chemometrics, 2022
Abstract In modern data analysis, there is an increasing availability of datasets with numerous variables. Linear models that deal with abundant predictor variables often have poor performance because they tend to produce large variances. As well known, partial least squares (PLS) regression standouts because it is serviceable even if
Zhonghao Xie, Xi'an Feng, Limin Li, Xiaojing Chen   +3 more
openaire   +1 more source

Partial Least‐Squares Regression

2013
This chapter presents the most widely applied and, probably, satisfactory multivariate regression method used nowadays: partial least squares (PLS). Graphical explanations of many concepts are given to complement the more formal mathematical background. Several approaches to solving current problems are suggested.
José Manuel Andrade‐Garda, Alatzne Carlosena‐Zubieta, Ricard Boqué‐Martí, Joan Ferré‐Baldrich   +3 more
openaire   +1 more source

A Reformulation of the Partial Least Squares Regression Algorithm

SIAM Journal on Scientific Computing, 1994
Let \(X = (x_ 1,\dots,x_ k)\), where \(x_ 1,\dots,x_ k\) are \(n\)- dimensional vectors (independent variables). Also available is an associated \(n\)-dimensional vector \(y\) (dependent variable). One of the main aims of linear regression is to predict the values of the dependent variable using a linear combination of the independent variables ...
openaire   +1 more source

The objective function of partial least squares regression

Journal of Chemometrics, 1998
A simple objective function in terms of undeflated X is derived for the latent variables of multivariate PLS regression. The objective function fits into the basic framework put forward by Burnham et al. (J. Chemometrics, 10, 31–45 (1996)). We show that PLS and SIMPLS differ in the constraint put on the length of the X-weight vector.
ter Braak, C.J.F., de Jong, S.
openaire   +2 more sources