Interaction-Based Learning for High-Dimensional Data with Continuous Predictors
High-dimensional data, such as that relating to gene expression in microarray experiments, may contain substantial amount of useful information to be explored. However, the information, relevant variables and their joint interactions are usually diluted by noise due to a large number of non-informat...
Main Author: | Huang, Chien-Hsun |
---|---|
Language: | English |
Published: |
2014
|
Subjects: | |
Online Access: | https://doi.org/10.7916/D8X928CH |
Similar Items
-
A univariate two-sample nonparametric test for dispersion and a class of bivariate two-sample nonparametric tests for location.
by: Chai, Shu-ping
Published: (1972) -
Nonparametric ranking and selection procedures /
by: Lee, Young Jack
Published: (1974) -
Comparison of Proposed K Sample Tests with Dietz's Test for Nondecreasing Ordered Alternatives for Bivariate Normal Data
by: Zhao, Yanchun
Published: (2018) -
Statistical methods for the detection of non-technical losses: a case study for the Nelson Mandela Bay Municipality
by: Pazi, Sisa
Published: (2017) -
Partition clustering of High Dimensional Low Sample Size data based on P-Values
by: Von Borries, George Freitas
Published: (2008)