Article

Consistence Condition of Kernel Selection In Regular Linear Kernel Regression

2022-04-14

Abstract excerpt

Kernel regression is widely used in biology and economy, because it is more adaptable to complex laws than linear regression, and it has better interpretability than many methods in deep learning. In highdimensional area, l1-norm penalization is a common method for variable selection, which may be derived from the excellent performance of the lasso algorithm. Although it seems natural to generalize from consistenc...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 70a4b051-eeba-5372-bc73-5de46fbf200f
DOI: 10.21203/rs.3.rs-1556479/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 8 closest

Consistence Condition of Kernel Selection In Regular Linear Kernel RegressionDOI 10.21203/rs.3.rs-1556479/v1Read

Topology and Geometry in Machine Learning for Logistic RegressionSemantic proximity Read

L2-norm multiple kernel learning and its application to biomedical data fusion.Semantic proximity Read

Rayleigh-quotient-based Data Classification Method for Machine LearningSemantic proximity Read

Comparison of Landslide Susceptibility Mapping Based on Two Machine Learning Algorithm ModelsSemantic proximity Read

YOLOv8n-DSRS:Road Disease Detection Based on Multiscale Dual-Path Feature Reorganization with Selective Kernel NetworksSemantic proximity Read

FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease associationSemantic proximity Read

Network regularized Accelerated Failure Time Models for Robust Biomarker IdentificationSemantic proximity Read

MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA-disease association prediction.Semantic proximity Read