Neural networks have been widely used as a tool for regression. They are capable of approximating any function and they do not require any assumption about the distribution of the data. The most commonly used architectures for regression are the feedforward neural networks with one or more hidden layers. In this paper, we present a network pruning algorithm which determines the number of units ...

A reshaping berm breakwater is a type of rubble mound breakwater in which, its seaward slope is allowed to reshape under wave attacks. There are some key parameters in the reshaped seaward profiles, which can schematize the reshaped profile of a berm breakwater. A total of 412 test results was used directly to cover the impact of sea state conditions and structural parameters on these reshaping...

Bayesian Additive Regression Trees (BART) are non-parametric models that can capture complex exogenous variable effects. In any regression problem, it is often of interest to learn which variables most active. Variable activity in BART usually measured by counting the number times a tree splits for each variable. Such one-way counts have advantage fast computations. Despite their convenience, s...

We propose a tree-based algorithm (μCART) for classification and regression problems in the context of functional data analysis, which allows to leverage measure learning multiple splitting rules at node level, with objective reducing error while retaining interpretability tree. For each internal node, our main contribution is idea weighted space by means constrained convex optimization, then u...