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This paper presents online topic model (OLDA), a topic model that automatically captures the thematic patterns and identifies emerging topics of text streams and their changes over time. Our approach allows the topic modeling framework, specifically the latent Dirichlet allocation (LDA) model, to work in an online fashion such that it incrementally builds an up-to-date model (mixture of topics per...
In real-world data mining applications, one often has access to multiple datasets that are relevant to the task at hand. However, learning from such datasets can be difficult as they are often drawn from different domains, i.e., not identically distributed or differ in class or feature sets. In this paper, we consider the problem of learning the class structures %, unique and shared, of related domains...
To cope with concept drift, we paired a stable online learner with a reactive one. A stable learner predicts based on all of its experience, whereas are active learner predicts based on its experience over a short, recent window of time. The method of paired learning uses differences in accuracy between the two learners over this window to determine when to replace the current stable learner, since...
Recognizing and analyzing change is an important human virtue because it enables us to anticipate future scenarios and thus allows us to act pro-actively. One approach to understand change within a domain is to analyze how models and patterns evolve. Knowing how a model changes over time is suggesting to ask: Can we use this knowledge to learn a model in anticipation, such that it better reflects...
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