Συντάχθηκε 08-04-2014 09:02
από Eleni Stamataki
Email συντάκτη: estamataki<στο>tuc.gr
Ενημερώθηκε:
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Ιδιότητα: σύνταξη/αποχώρηση υπάλληλος.
Σχολή Ηλεκτρονικών Μηχανικών & Μηχανικών Υπολογιστών
ΠΑΡΟΥΣΙΑΣΗ ΜΕΤΑΠΤΥΧΙΑΚΗΣ ΔΙΑΤΡΙΒΗΣ
Αλέξανδρου Γεωργογιάννη
Με θέμα:
«Regularized optimization applied to clustering and estimation of multiple undirected graphical models.»
Τρίτη 8 Απριλίου 2014, Ώρα : 17:00 μμ,
Αίθουσα Συνεδριάσεων Σχολής ΗΜΜΥ, Κτίριο Επιστημών,
Πολυτεχνειούπολη
Εξεταστική Επιτροπή:
Καθ. Διγαλάκης Βασίλειος(Επιβλέπων)
Καθ. Λιάβας Αθανάσιος
Αν. Καθ. Λαγουδάκης Μιχαήλ
Abstract
Since its earliest days as a discipline, machine learning has made use of optimization formulations and algorithms. Likewise, machine learning has contributed to optimization, driving the development of new optimization approaches that address the significant challenges presented by machine learning applications. This influence continues to deepen, producing a growing literature at the intersection of the two fields while attracting leading researchers to the effort. While techniques proposed twenty years ago continue to be refined, the increased complexity, size, and variety of today’s machine learning models demand a principled reassessment of existing assumptions and techniques. This thesis makes a small step toward such a reassessment. It describes novel contexts of established frameworks such as convex relaxation, splitting methods, and regularized estimation and how we can use them to solve significant problems in data mining and statistical learning.
