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Found 5 papers, 1 papers with code
Function Trees: Transparent Machine Learning
Given the inputs and corresponding function values, a function tree is constructed that can be used to rapidly identify and compute all of the function's main and interaction effects up to high order.
Lockout: Sparse Regularization of Neural Networks
Many regression and classification procedures fit a parameterized function $f(x;w)$ of predictor variables $x$ to data $\{x_{i}, y_{i}\}_1^N$ based on some loss criterion $L(y, f)$.
Predicting Regression Probability Distributions with Imperfect Data Through Optimal Transformations
Usually a particular x-vector does not specify a repeatable value for y, but rather a probability distribution of possible y--values, p(y|x).
Contrast Trees and Distribution Boosting
Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output.
Predictive learning via rule ensembles
General regression and classification models are constructed as linear combinations of simple rules derived from the data.
