Computational Advertising, popularly known as online advertising or Web advertising, refers
to finding the most relevant ads matching a particular context on the Web. The context …

Methods for learning decision rules are being successfully applied to many problem
domains, in particular when understanding and interpretation of the learned model is …

This article presents GuideR, a user-guided rule induction algorithm, which overcomes the
largest limitation of the existing methods—the lack of the possibility to introduce user's …

Induction of decision rules plays an important role in machine learning. The main advantage
of decision rules is their simplicity and human-interpretable form. Moreover, they are …

Prediction rule ensembles (PREs) are sparse collections of rules, offering highly
interpretable regression and classification models. This paper shows how they can be fitted …

Interpretability is becoming increasingly important for predictive model analysis.
Unfortunately, as remarked by many authors, there is still no consensus regarding this …

Paid advertisements displayed alongside search results constitute a major source of income
for search companies. Optimizations leading to more clicks on ads are a target goal shared …

We consider the problem of learning a sparse rule model, a prediction model in the form of a
sparse linear combination of rules, where a rule is an indicator function defined over a hyper …

The paper presents results of application of a machine learning method, namely the
induction of classification and regression rules, for seismic hazard prediction in coal mines …

In this paper, we propose a novel approach for learning regression rules by transforming the
regression problem into a classification problem. Unlike previous approaches to regression …