Large Margin LAD Models and LAD-based Regression
We discuss some recent developments in Logical Analysis of
Data (LAD), with a focus on (i) the use of the concept of
large margin classifiers to build parameter-free LAD
classification models, and (ii) the extension of the LAD
methodology to address regression problems.
Both ideas rely on simple optimization models, strongly based
on the original formulation proposed by Hammer et al. in the
seminal LAD papers.
We compare the proposed techniques to standard classification
and regression techniques through a series of experiments on
datasets from the UCI repository. Finally, we discuss some
extensions of this work and of the LAD methodology