Campus Event Calendar
Event Entry
What and Who
Probabilistic modeling and learning in relational domains
Manfred Jaeger
Uni Aalborg, Daenemark
Talk
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, RG2
MPI Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Tuesday, 22 April 2008
14:00
45 Minutes
E1 4
021
Saarbrücken
Abstract
In the last few years we have seen the emergence of the new field of
"Statistical Relational Learning", also called "Probabilistic Logic Learning",
or "Relational Data Mining". The plethora of labels results from the fact that this
field is a confluence of several quite distinct strands of research: one line of research
is the combination of probabilistic graphical models with more abstract, logic-based,
knowledge representation languages. A second line of research contributing to the new
field are probabilistic extensions of inductive logic programming techniques. Finally
(and perhaps most significantly), probabilistic logic learning joins together these
AI traditions with those areas in machine learning that are concerned with learning from
structured data (graphs, sequences, relational databases, ...).
In my talk I will give a biased overview over some central issues in probabilistic logic
learning: I will describe two representation languages for probabilistic logic models --
Relational Bayesian Networks and Markov Logic Networks -- and discuss expressivity and
complexity of these languages. I will then discuss techniques and application for learning
these models from data.
"Statistical Relational Learning", also called "Probabilistic Logic Learning",
or "Relational Data Mining". The plethora of labels results from the fact that this
field is a confluence of several quite distinct strands of research: one line of research
is the combination of probabilistic graphical models with more abstract, logic-based,
knowledge representation languages. A second line of research contributing to the new
field are probabilistic extensions of inductive logic programming techniques. Finally
(and perhaps most significantly), probabilistic logic learning joins together these
AI traditions with those areas in machine learning that are concerned with learning from
structured data (graphs, sequences, relational databases, ...).
In my talk I will give a biased overview over some central issues in probabilistic logic
learning: I will describe two representation languages for probabilistic logic models --
Relational Bayesian Networks and Markov Logic Networks -- and discuss expressivity and
complexity of these languages. I will then discuss techniques and application for learning
these models from data.
Contact
Roxane Wetzel
--email hidden
passcode not visible
logged in users only
Roxane Wetzel, 04/15/2008 16:37
Roxane Wetzel, 04/14/2008 14:10 -- Created document.
Roxane Wetzel, 04/14/2008 14:10 -- Created document.