Bending test on large-scale GLT beams with well-known beam setup using machine grading indicator

Large-scale Machine Learning using DryadLINQ

The main motivation behind the development of DryadLINQ was to make it easier for non-specialists to write general purpose, scalable programs that can operate on very large input datasets. In order to appeal to non-specialists we designed the programming interface to use a high level of abstraction that insulates the programmer from most of the detail and complexity of parallel and distributed ...

Configuring large-scale storage using a middleware with machine learning

ions utilized over configuration parameters Each deployment of the SAN configuration middleware will see a different underlying SAN infrastructure (i.e. the myriad different data centres of different cloud providers), each with its own custom set of configuration parameters. Transporting these data to and from the best practice repository will require the use of an abstract data format for SAN ...

Recent Advances in Large-Scale Peer Grading

Bending of Beams with Unsymmetrical Sections

Large Scale Machine Learning

Cette thèse aborde de façon générale les algorithmes d'apprentissage, avec un intérêt tout particulier pour les grandes bases de données. Après avoir for-mulé leprobì eme de l'apprentissage demanì ere mathématique, nous présentons plusieurs algorithmes d'apprentissage importants, en particulier les Multi Layer Perceptrons, les Mixture d'Experts ainsi que les Support Vector Machines. Nous consid...