Data Mining for the XXI Century

Speaker:  João Gama – Porto, Portugal
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing


Nowadays, there are applications where data is best modeled not as persistent tables, but rather as transient data streams. In this talk, we discuss the limitations of current machine learning and data mining algorithms and 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 take these constraints into account. We identify the main issues and current challenges that emerge in learning from data streams and present open research lines for further development.

About this Lecture

Number of Slides:  50
Duration:  45 minutes
Languages Available:  English, Portuguese
Last Updated: 

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