Diversity and accuracy are the two key factors that decide the ensemble generalization error. Constructing a good ensemble method by balancing these two factors is difficult, because increasing diversity is at the cost of reducing accuracy normally. In order to improve the performance of an ensemble while avoiding the difficulty derived of balancing diversity and accuracy, we propose a novel me...

A technique is presented for subband adaptive ltering with nonuniform lter banks. The bandwidth allocations of the subband analysis and synthesis lters are adapted to the spectral characteristics of the input data in such a manner as to minimize an objective function built from the subband error powers. The nonuniform lter bank structure allows for fast convergence times for high order systems ...

One way to define the “randomness” of a fixed individual sequence is to ask how hard it is to predict. When prediction error is measured via squared loss, it has been established that memoryless sequences (which are, in a precise sense, hard to predict) have some of the stochastic attributes of truly random sequences. In this paper, we ask how changing the loss function used changes the set of ...

Worst-case analysis of the game assumes, by definition, that the adversary is trying to minimize the learner’s regret without any restrictions on the resources it uses while doing so. In practice, however, it may not be necessary (or indeed desirable) to get bounds of this kind—real-world data are typically generated by processes of bounded computational power, memory, etc., and it would be use...

The purpose of this study is to identify the effect of Ordinary Kriging (OK) and Inverse Distance Weighted (IDW) estimation methods for the delineation of mineralized zones based on subsurface data using concentration–volume (C–V) multifractal modeling in the Dardevey iron ore deposit, NE Iran. Variograms and anisotropic ellipsoid were generated for the Fe distribution by the above estimation m...

A family of classification algorithms generated from Tikhonov regularization schemes are considered. They involve multi-kernel spaces and general convex loss functions. Our main purpose is to provide satisfactory estimates for the excess misclassification error of these multi-kernel regularized classifiers. The error analysis consists of two parts: regularization error and sample error. Allowin...