[Solomonov Seminar] 180. Solomonov seminar

[Marko Grobelnik]

Vabim vas na 180. Solomonov seminar, ki bo v torek 20. februarja,
ob 13:00 uri v Veliki predavalnici IJS.

Predaval bo Bernard Zenko iz IJS o svoji doktorski temi.

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Bernard Zenko, IJS
              Learning predictive clustering rules

Predictive clustering is based on ideas from two machine learning subareas, 
predictive modeling and clustering. Methods for predictive clustering enable 
us to construct models for predicting multiple target variables, which are 
normally simpler and more comprehensible than the corresponding collection 
of models, each predicting a single variable. To this end, predictive clustering 
has been restricted to decision tree methods. Our goal is to extend this approach 
to methods for learning rules. We have developed a generalized version of 
the covering algorithm that enables learning of ordered or unordered rules, 
on single or multiple target classification or regression domains. Performance 
of the new method compares favorably to existing methods. Comparison of 
single target and multiple target prediction models shows that multiple target 
models offer comparable performance and drastically lower complexity than 
the corresponding collections of single target models.