Welcome!
To use the personalized features of this site, please log in or register.
If you have forgotten your username or password, we can help.
|
 |
Learning Recursive Concepts with Anomalies
| |
|
Learning Recursive Concepts with Anomalies
Gunter Grieser4  , Steffen Lange5 and Thomas Zeugmann6
| (4) |
Technische Universität Darmstadt, Fachbereich Informatik, Alexanderstr. 10, 64283 Darmstadt, Germany |
| (5) |
Universität Leipzig, Institut für Informatik, Augustusplatz 10-11, 04109 Leipzig, Germany |
| (6) |
Medizinische Universität Lübeck, Institut für Theoretische Informatik, Wallstr. 40, 23560 Lübeck, Germany |
Abstract
This paper provides a systematic study of inductive inference of indexable concept classes in learning scenarios in which
the learner is successful if its final hypothesis describes a finite variant of the target concept - henceforth called learning
with anomalies. As usual, we distinguish between learning from only positive data and learning from positive and negative
data.
We investigate the following learning models: finite identification, conservative inference, set-driven learning, and behaviorally
correct learning. In general, we focus our attention on the case that the number of allowed anomalies is finite but not a
priori bounded. However, we also present a few sample results that affect the special case of learning with an a priori bounded number of anomalies. We provide characterizations of the corresponding models of learning with anomalies in terms
of finite tell-tale sets. The varieties in the degree of recursiveness of the relevant tell-tale sets observed are already
sufficient to quantify the differences in the corresponding models of learning with anomalies.
In addition, we study variants of incremental learning and derive a complete picture concerning the relation of all models
of learning with and without anomalies mentioned above.
Fulltext Preview (Small, Large)
 References secured to subscribers.
|
|
|
|
|
|