Search

In this study, we introduce an ensemble-based approach for online machine learning. The ensemble of base classifiers in our approach is obtained by learning Naive Bayes classifiers on different training sets which are generated by projecting the original training set to lower dimensional space. We propose a mechanism to learn sequences of data using data chunks paradigm. The experiments conducted on a number of UCI datasets and one synthetic dataset demonstrate that the proposed approach performs significantly better than some well-known online learning algorithms., Comment: 19 pages, 3 figures

Relaxation of initially out-of-equilibrium rough interfaces in presence of thermal noise is investigated using Langevin formalism. During thermal equilibration towards the well-known roughening regime, three scaling regimes observed over three successive ranges of length-scales are evidenced: thermal roughening (late stage) at small scales, transient smoothening at intermediate scales and remnant of the initial conditions at large scales. A generalization of the Family-Vicsek scaling is found for the smoothening regime. A distinctive feature of the transient smoothening regime resides in the existence of a super-universal exponent, i.e. independent of the considered model. This approach allows interpreting a series of AFM images of sapphire surfaces showing the thermal evolution of initially rough step edges.