Recent Advances in Learning from Data Streams
- RC Trust
Abstract - Learning from data streams is a hot topic in machine learning and data mining. In this talk, we present two different problems and discuss streaming techniques to solve them. The first problem is the application of data stream techniques to predictive maintenance. We propose a two layer neuro-symbolic approach to explain black-box models. The explanations are oriented toward equipment failures. For the second problem, we present a streaming algorithm for online hyper-parameter tuning. The Self hyper-Parameter Tunning (SPT) algorithm is an optimization algorithm for online hyper-parameter tuning from non-stationary data streams. SPT works as a wrapper over any streaming algorithm and can be used for classification, regression, and recommendation.
Prof. Dr. João Gama
Bio - João Gama is a Full Professor at the School of Economics, University of Porto, Portugal. He received his Ph.D. in Computer Science from the University of Porto in 2000. He is EurAI Fellow, IEEE Fellow, and the Asia-Pacific AI Association Fellow. He is a member of the board of directors of the LIAAD, a group belonging to INESC Tec. His main contributions are learning from data streams, where he has an extensive list of publications. He is the Editor-in-Chief of the International Journal of Data Science and Analytics, published by Springer.