  The field of machine learning (ML) is concerned with the question of how to
construct algorithms that automatically improve with experience. In recent
years many successful ML applications have been developed, such as datamining
programs, information-filtering systems, etc. Although ML algorithms allow the
detection and extraction of interesting patterns of data for several kinds of
problems, most of these algorithms are based on quantitative reasoning, as they
rely on training data in order to infer so-called target functions.
  In the last years defeasible argumentation has proven to be a sound setting
to formalize common-sense qualitative reasoning. This approach can be combined
with other inference techniques, such as those provided by machine learning
theory.
  In this paper we outline different alternatives for combining defeasible
argumentation and machine learning techniques. We suggest how different aspects
of a generic argument-based framework can be integrated with other ML-based
approaches.
