In this Comment we point out that the semi-Poisson is well suited only as a reference point for the so-called "intermediate statistics," which cannot be interpreted as a universal ensemble, like the Gaussian orthogonal ensemble or the Poissonian statistics. In Ref. 2 it was proposed that the nearest-neighbor distribution P(s) of the spectrum of a Poissonian distributed matrix perturbed by a ran...

In this paper, we address semi-supervised sentiment learning via semi-stacking, which integrates two or more semi-supervised learning algorithms from an ensemble learning perspective. Specifically, we apply metalearning to predict the unlabeled data given the outputs from the member algorithms and propose N-fold cross validation to guarantee a suitable size of the data for training the meta-cla...

accurate quantitative precipitation forecasts (qpfs) have been always a demanding and challenging job in numerical weather prediction (nwp). the outputs of ensemble prediction systems (epss) in the form of probability forecasts provide a valuable tool for probabilistic quantitative precipitation forecasts (pqpfs). in this research, different configurations of wrf and mm5 meso-scale models form ...

We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-convex losses, extending a recent analysis of the problem for misclassification e...

We address the problem of aggregating an ensemble of predictors with known loss bounds in a semi-supervised binary classification setting, to minimize prediction loss incurred on the unlabeled data. We find the minimax optimal predictions for a very general class of loss functions including all convex and many non-convex losses, extending a recent analysis of the problem for misclassification e...

We develop an approach to apply Wang-Landau algorithm to multicomponent alloys in semigrand-canonical ensemble. Although the Wang-Landau algorithm has great advantages over conventional sampling methods, there are few applications to alloys. This is because calculating compositions in semi-grand-canonical ensemble using the Wang-Landau algorithm requires a multidimensional density of states in ...

In recent years, the interest in semi-supervised learning has increased, combining supervised and unsupervised learning approaches. This is especially valid for classification applications in remote sensing, while the data acquisition rate in current systems has become fairly large considering highand very-high resolution data; yet on the other hand, the process of obtaining the ground truth da...

Semi-supervised learning (SSL) methods attempt to achieve better classification of unseen data through the use unlabeled than can be achieved by from available labeled alone. Most SSL require user familiarize themselves with novel, complex concepts and ensure underlying assumptions made these match problem structure, or they risk a decrease in predictive performance. In this paper, we present r...

We propose the weakly supervised MultiExperts Model (MEM) for analyzing the semantic orientation of opinions expressed in natural language reviews. In contrast to most prior work, MEM predicts both opinion polarity and opinion strength at the level of individual sentences; such fine-grained analysis helps to understand better why users like or dislike the entity under review. A key challenge in...