| Title | Learning with Kernels for Streams of Structured Data |
| Speaker | Prof. Alessandro Sperduti |
| Chair | Barbara Hammer |
Abstract
Learning from streaming data represents an important and challenging task. Maintaining an accurate model, while the stream goes by, requires a smart way for tracking data changes through time. This challenge is harder if the stream is constituted by structured data, such as trees and graphs.
In the talk, I motivate why it makes sense to consider streams of structured data. Moreover, after recalling the basic computational requirements for processing streams, I discuss the additional difficulties that are encountered when using kernels for structured data.
Subsequently, I report on recent advances on the efficient use of kernels for structured data on streams of trees and graphs. All these techniques are based on efficient representation of the learning model and/or of the feature space.
Finally, I close the talk with an example of a promising direction of research where a well known data mining technique, i.e. Lossy Counting, can be integrated into the learning process so to dramatically speed up training while preserving performances.
Biography
Alessandro Sperduti received his education from the University of Pisa, Italy. (Laurea and Doctoral degrees in 1988 and 1993, respectively, all in Computer Science.) In 1993 he spent a period at the International Computer Science Institute, Berkeley, supported by a postdoctoral fellowship. In 1994 he moved back to the Computer Science Department, University of Pisa, where he was an Assistant Professor, and Associate Professor. Currently, he is a Full Professor at the Department of Mathematics, University of Padova. His research interests include machine learning, neural networks, learning in structured domains, hybrid systems, data and process mining. In the field of hybrid systems his work has focused on the integration of symbolic and connectionist systems. He contributed to the organization of several workshops on this subject and he served also in the program committee of several conferences on Neural Networks, Machine Learning, Artificial Intelligence, and Information Retrieval. Alessandro Sperduti is the author of more than 150 refereed papers mainly in the areas of Neural Networks, Pattern Recognition and Kernel Methods. Moreover, he gave several tutorials within international schools and conferences, such as IJCAI 97, IJCAI 99, IJCAI 01, WCCI 12. He has been plenary speaker at ICANN 2001, WSOM 2007, ISNN 2012. He acted as a Guest Co-Editor of journal special issues on Connectionist Models for Learning in Structured Domains. He has been chair of the Data Mining and Neural Networks Technical Committees of IEEE CIS, and Associate Editor of IEEE TNNLS. Currently he is in the editorial board of journals such as AI Communications, Theoretical Computer Science (section on Natural Computing), IEEE Intelligent Systems Magazine. He is an IEEE senior member.
