Data-hora: 14:00, 13 de Março de 2017

Local: H-324-B, PESC

Abstract

Nowadays, there are applications in which the data are modelled best not as persistent tables, but rather as transient data streams. In this keynote, we discuss the limitations of current machine learning and data mining algorithms. We discuss the fundamental issues in learning in dynamic environments like learning decision models that evolve over time, learning and forgetting, concept drift and change detection. Data streams are characterized by huge amounts of data that introduce new constraints in the design of learning algorithms: limited computational resources in terms of memory, processing time and CPU power.

In this talk, we present some illustrative algorithms designed to taking these constrains into account. We identify the main issues and current challenges that emerge in learning from data streams, and present open research lines for further developments.

Short Bio

João Gama received his Licenciado degree from the Fac. of Engineering of the University of Porto, Portugal. In 2000 he received his Ph.D. degree in Computer Science from the Faculty of Sciences of the same University. He joined the Faculty of Economy where he holds the position of Associate Professor, He is also a senior researcher at LIAAD, a group belonging to INESC Porto. He has worked in projects and authored papers in areas related to machine learning, data streams and adaptive learning systems and is a member of the editorial board of international journals in his area of expertise.