Keynote Title: The Interplay of Statistical and Structural Pattern Recognition from a Machine Learning Perspective
Keynote Lecturer: Dr. Tiberio Caetano
Keynote Chair: Dr. Francis Bach
Presented on: 08-02-2012
Abstract: The pattern recognition community has traditionally been segmented into two camps: the statistical and the structural/syntactical. While the former has focused on data-driven estimation issues, the latter has devoted particular attention to representational issues. In this talk I will discuss how these approaches have been converging in recent years thanks to developments in machine learning. In particular I will present how traditional questions asked in the structural camp, such as the matching of structural representations of objects, can naturally lend themselves to a statistical, data-driven approach. I will show several benefits of such integrated perspective, both in terms of improved accuracy and improved efficiency. I will show empirical results of the proposed techniques in different application domains, including graph matching in computer vision and image tagging under taxonomic prior knowledge. Finally, I will briefly present my personal perspective on future directions for the field.
Presented at the following Conference: ICPRAM, International Conference on Pattern Recognition Applications and Methods
Conference Website: icpram.org/
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