An empirical comparison of machine learning algorithms for generic image classification

There is a general need to make the training of machine learning models effi- cient, with respect to different parameters such as the size of these networks and the time and

From several years, a number of researches have been conducted to design robust No-Reference Image Quality Assessment (NR-IQA) algorithms, claiming to have made headway in

The presented approach is also applied to financial time series from the credit default swaps mar- ket whose prices dynamics are usually modelled by random walks according to

In this study we perform a way to classify the Zwicky Transient Facility Public Data Release 1 catalog onto variable stars of different types.. As the priority classes we set

Dictionary learning has been widely used in reconstruction tasks. In its classical formulation, the goal is to learn a set of atoms directly from data. Using such unsupervised

Chapitre III Résultats Tableau 26: surfaces (mm 2), fréquences relatives (F%), taux de consommation (T%) et indices d'attraction (IA) des espèces végétales trouvées dans les

Discriminative Restricted Boltzmann Machines appear to yield slightly better accuracy than Support Vector Machines, and are less sensitive to the choice of signal preprocessing

On the other hand, the application of penalized optimal scoring to block clustering looks more complex, but as block clustering is typically defined as a mixture model whose