Academy of Sciences, Czech Republic
Title: More Efficient Data Mining by Means of Meta-Learning
Meta-learning approach utilizes previous experience with learning algorithms to improve the results of machine learning applications. By applying machine learning principles to machine learning process itself it is possible to automate tasks that have so far been dependent on human experience and experiments. One of the tasks of meta-learning for data mining is to recommend a suitable method for new data sets. We will focus on generating and testing complete workflows embedding machine learning methods together with preprocessing and their combinations, such as ensembles.
R. Neruda received his Ph.D. in Theoretical Computer Science from Czech Academy of Sciences in 1998. He is a researcher at Institute of Computer Science of Czech Academy of Sciences in Prague, and a lecturer at Charles University in Prague. His research interests include neural networks, evolutionary algorithms and hybrid machine learning models.