Μεταπτυχιακές Διατριβές
Μόνιμο URI για αυτήν τη συλλογήhttps://dspace.library.tuc.gr/handle/123456789/121
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Πλοήγηση Μεταπτυχιακές Διατριβές ανά Συγγραφέα "Digalakis Vasilis"
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Δημοσίευση Hyper spectral data estimation from power dimensionality experimental imaging(Technical University of Crete, 2014) Iliou Dimitrios; Ηλιου Δημητριος; Balas Costas; Μπαλας Κωστας; Garofalakis Minos; Γαροφαλακης Μινως; Digalakis Vasilis; Διγαλακης ΒασιληςWe report the first real time modular spectral and color imaging system based on the combination of snapshot spectral imaging, spectral estimation and color reproduction algorithms. A limited number of spectral bands are captured simultaneously, with the aid of specially designed camera, which are subsequently processed with spectral estimation algorithms to obtain a full spectrum per image pixel. We have succeeded to demonstrate complete spectral cube calculation and display of millions of spectra in real-time and to remove trade-off between spectral and spatial resolution. Besides accurate spectral mapping, our approach enables also reliable and device-independent color reproduction based on complete, per-pixel spectra. These achievements hold the promise to provide an indispensable tool in nondestructive analysis and in noninvasive diagnosis.Δημοσίευση Regularized optimization applied to clustering and joint estimation of multiple undirected graphical models(Πολυτεχνείο Κρήτης, 2014) Georgogiannis Alexandros; Γεωργογιαννης Αλεξανδρος; Digalakis Vasilis; Διγαλακης Βασιλης; Liavas Athanasios; Λιαβας Αθανασιος; Lagoudakis Michael; Λαγουδακης ΜιχαηλSince 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 develop- ment 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. The thesis is organised in two parts. In the first part, we present a new clustering algorithm. The task of clustering aims at discovering structures in data. This algorithm is an extension of recently proposed convex relaxations of k-means and hierarchical clustering. In the second part, we present a new algorithm for discovering dependencies among common variables in multiple undirected graphical models. Graphical models are useful for the description and modelling of multivariate systems. In the appendix, we comment on a core problem underlying the whole study and we give an alternative solution based on recent advances in convex optimization.