3 research outputs found
Structure Selection from Streaming Relational Data
Statistical relational learning techniques have been successfully applied in
a wide range of relational domains. In most of these applications, the human
designers capitalized on their background knowledge by following a
trial-and-error trajectory, where relational features are manually defined by a
human engineer, parameters are learned for those features on the training data,
the resulting model is validated, and the cycle repeats as the engineer adjusts
the set of features. This paper seeks to streamline application development in
large relational domains by introducing a light-weight approach that
efficiently evaluates relational features on pieces of the relational graph
that are streamed to it one at a time. We evaluate our approach on two social
media tasks and demonstrate that it leads to more accurate models that are
learned faster
Towards clausal discovery for stream mining
With the increasing popularity of data streams it has become time to adapt logical and relational learning techniques for dealing with streams.
In this note, we present our preliminary results on upgrading the clausal discovery paradigm towards the mining of streams. In this setting, there is a stream of interpretations and the goal is to learn a clausal theory that is satisfied by these interpretations. Furthermore, in data streams the interpretations can be read (and processed) only once.status: publishe
Towards clausal discovery for stream mining
Abstract. With the increasing popularity of data streams it has become time to adapt logical and relational learning techniques for dealing with streams. In this note, we present our preliminary results on upgrading the clausal discovery paradigm towards the mining of streams. In this setting, there is a stream of interpretations and the goal is to learn a clausal theory that is satisfied by these interpretations. Furthermore, in data streams the interpretations can be read (and processed) only once.