Research Areas ǀ Machine Learning
In the area of Maschine Learning, we are focusing on multi-target prediction and representation learning.
In the field of multi-target prediction, we are developing various methods addressing different machine learning tasks. (1) We are designing novel methods for learning oblique decision trees for simple supervised tasks such as classification and regression, as well as for more complex supervised tasks from structured output prediction such as multi-label classification, hierarchical multi-label classification, and multi-target regression. (2) We are extending these methods towards semi-supervised learning for both simple and complex learning tasks (such as structured output prediction). (3) We are developing a methodology for fusing different evaluation measures in the context of recommender systems. (4) We are performing a study to analyze and explain the performance of multi-label classification methods with data set properties.
We are also addressing the topic of representation learning, where we are developing data mining methods for the analysis of heterogeneous data, and using them in several application domains.
Projects in the field of Machine Learning: