Survey on cyberspace security
H Zhang, W Han, X Lai, D Lin, J Ma, JH Li - Science China Information …, 2015 - Springer
Along with the rapid development and wide application of information technology, human
society has entered the information era. In this era, people live and work in cyberspace …
society has entered the information era. In this era, people live and work in cyberspace …
Just interpolate: Kernel “ridgeless” regression can generalize
Just interpolate: Kernel "Ridgeless" regression can generalize Page 1 The Annals of
Statistics 2020, Vol. 48, No. 3, 1329–1347 https://doi.org/10.1214/19-AOS1849 © Institute of …
Statistics 2020, Vol. 48, No. 3, 1329–1347 https://doi.org/10.1214/19-AOS1849 © Institute of …
Generalization properties of learning with random features
We study the generalization properties of ridge regression with random features in the
statistical learning framework. We show for the first time that $ O (1/\sqrt {n}) $ learning …
statistical learning framework. We show for the first time that $ O (1/\sqrt {n}) $ learning …
Optimal rates for the regularized least-squares algorithm
A Caponnetto, E De Vito - Foundations of Computational Mathematics, 2007 - Springer
We develop a theoretical analysis of the performance of the regularized least-square
algorithm on a reproducing kernel Hilbert space in the supervised learning setting. The …
algorithm on a reproducing kernel Hilbert space in the supervised learning setting. The …
[图书][B] Learning theory: an approximation theory viewpoint
F Cucker, DX Zhou - 2007 - books.google.com
The goal of learning theory is to approximate a function from sample values. To attain this
goal learning theory draws on a variety of diverse subjects, specifically statistics …
goal learning theory draws on a variety of diverse subjects, specifically statistics …
Kernel regularized least squares: Reducing misspecification bias with a flexible and interpretable machine learning approach
J Hainmueller, C Hazlett - Political Analysis, 2014 - cambridge.org
We propose the use of Kernel Regularized Least Squares (KRLS) for social science
modeling and inference problems. KRLS borrows from machine learning methods designed …
modeling and inference problems. KRLS borrows from machine learning methods designed …
Distributed learning with regularized least squares
We study distributed learning with the least squares regularization scheme in a reproducing
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …
kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a …
Learning theory estimates via integral operators and their approximations
The regression problem in learning theory is investigated with least square Tikhonov
regularization schemes in reproducing kernel Hilbert spaces (RKHS). We follow our …
regularization schemes in reproducing kernel Hilbert spaces (RKHS). We follow our …
Machine learning approaches for improving condition-based maintenance of naval propulsion plants
Availability, reliability and economic sustainability of naval propulsion plants are key
elements to cope with because maintenance costs represent a large slice of total …
elements to cope with because maintenance costs represent a large slice of total …
[PDF][PDF] Optimal Rates for Regularized Least Squares Regression.
I Steinwart, DR Hush, C Scovel - COLT, 2009 - learningtheory.org
We establish a new oracle inequality for kernelbased, regularized least squares regression
methods, which uses the eigenvalues of the associated integral operator as a complexity …
methods, which uses the eigenvalues of the associated integral operator as a complexity …