Improved Multilevel Regression with Poststratification through Machine Learning (autoMrP)

Improved Behavioral Animation Through Regression

Behavioral and cognitive modeling have become popular for creating autonomous, selfanimating virtual characters. However, existing techniques have some weaknesses. In particular, cognitive models are usually very computationally expensive, limiting their usefulness. Also, behavioral and cognitive models can behave unexpectedly since it may be impossible to exhaustively test the model for the en...

bartMachine: Machine Learning with Bayesian Additive Regression Trees

We present a new package in R implementing Bayesian additive regression trees (BART). The package introduces many new features for data analysis using BART such as variable selection, interaction detection, model diagnostic plots, incorporation of missing data and the ability to save trees for future prediction. It is significantly faster than the current R implementation, parallelized, and cap...

Regularized extreme learning machine for regression with missing data

This paper proposes a method which is the advanced modification of the original Extreme Learning Machine with a new tool to solve the missing data problem. It uses a cascade of L1 penalty (LARS) and L2 penalty (Tikhonov regularization) on ELM to regularize the matrix computations and hence make the MSE computation more reliable, and on the other hand, it estimates the expected pairwise distance...

Multivariate Regression and Machine Learning with Sums of Separable Functions

Abstract. We present an algorithm for learning (or estimating) a function of many variables from scattered data. The function is approximated by a sum of separable functions, following the paradigm of separated representations. The central fitting algorithm is linear in both the number of data points and the number of variables, and thus is suitable for large data sets in high dimensions. We pr...

Learning textural concepts through multilevel symbolic transformations