Ensemble learning algorithms such as boosting can achieve better performance by averaging over the predictions of base hypotheses. However, existing algorithms are limited to combining only a finite number of hypotheses, and the generated ensemble is usually sparse. It is not clear whether we should construct an ensemble classifier with a larger or even infinite number of hypotheses. In additio...

Ensemble models combine two or more models to enable a more robust prediction, classification, or variable selection. This paper describes three types of ensemble models: boosting, bagging, and model averaging. It discusses go-to methods, such as gradient boosting and random forest, and newer methods, such as rotational forest and fuzzy clustering. The examples section presents a quick setup th...

We study the price dynamics of stocks traded in the NASDAQmarket by considering the statistical properties of an ensemble of stocks traded simultaneously. For each trading day of our database, we study the ensemble return distribution by extracting its first two central moments. According to previous results obtained for the NYSE market, we find that the second moment is a long-range correlated...

A bstract We consider the generalization of a matrix integral with arbitrary spectral curve ρ 0 ( E ) to 0+1D theory quantum mechanics (MQM). Using recent techniques for 1D systems at large- N , we formulate hydrodynamical effective eigenvalues. The result is simple 2D free boson BCFT on curved background, describing fluctuations eigenvalues around ), which now limit expectation value eigenvalu...