12 citations · 14 across the 3 of their papers we have counts for
4 papers
Conformal Prediction Intervals for Neural Networks Using Cross Validation
Saeed Khaki, Dan Nettleton
Neural networks are among the most powerful nonlinear models used to address supervised learning problems. Similar to most machine learning algorithms, neural networks produce poin…
Adjusting for Spatial Effects in Genomic Prediction
Xiaojun Mao, Somak Dutta, Raymond K. W. Wong +1
This paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genomic prediction, spatial effects often affect ph…
Regression-Enhanced Random Forests
Haozhe Zhang, Dan Nettleton, Zhengyuan Zhu
Random forest (RF) methodology is one of the most popular machine learning techniques for prediction problems. In this article, we discuss some cases where random forests may suffe…
The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data
Steven P. Lund, Dan Nettleton
When analyzing microarray data, hierarchical models are often used to share information across genes when estimating means and variances or identifying differential expression. Man…