As the reader can find out from the excellent book by Mehta [], it is well known that, if the probability P (M ∈ dM) satisfies the following two requirements: (i) invariance under conjugation by unitary transformations M 7→ UMU, (ii) the random variables Mii, Re Mij , Im Mij , 1 ≤ i < j ≤ N are independent, then V (z) is quadratic (Gaussian ensemble). For this ensemble and for very large N , th...

We present a freely available named-entity recognizer for Greek texts that identifies temporal expressions, person, and organization names. For temporal expressions, it relies on semi-automatically produced patterns. For person and organization names, it employs an ensemble of Support Vector Machines that scan the input text in two passes. The ensemble is trained using active learning, whereby ...

Future increases in flooding potential around the world’s coastlines from extreme sea level events is heavily dependent on projections of future global mean sea level (GMSL) rise. Yet, the two main approaches for projecting 21st century GMSL rise—i.e., process-based versus semi-empirical—give inconsistent results. Here, a novel hybrid approach to GMSL projection, containing a process-based ther...

The question why deep learning algorithms perform so well in practice has puzzled machine learning theoreticians and practitioners alike. However, most of well-established approaches, such as hypothesis capacity, robustness or sparseness, have not provided complete explanations, due to the high complexity of the deep learning algorithms and their inherent randomness. In this work, we introduce ...

We formalize the notion of a pseudo-ensemble, a (possibly infinite) collection of child models spawned from a parent model by perturbing it according to some noise process. E.g., dropout [9] in a deep neural network trains a pseudo-ensemble of child subnetworks generated by randomly masking nodes in the parent network. We examine the relationship of pseudo-ensembles, which involve perturbation ...

Ensemble learning is widely used to combine varieties of weak learners in order to generate a relatively stronger learner by reducing either the bias or the variance of the individual learners. Rotation forest (RoF), combining feature extraction and classifier ensembles, has been successfully applied to hyperspectral (HS) image classification by promoting the diversity of base classifiers since...

Text classification is a natural application domain for semisupervised learning, as labeling documents is expensive, but on the other hand usually an abundance of unlabeled documents is available. We describe a novel simple twostage scheme based on dagging which allows for utilizing the test set in model selection. The dagging ensemble can also be used by itself instead of the original classifi...

Graph structure is vital to graph based semi-supervised learning. However, the problem of constructing a graph that reflects the underlying data distribution has been seldom investigated in semi-supervised learning, especially for high dimensional data. In this paper, we focus on graph construction for semisupervised learning and propose a novel method called Semi-Supervised Classification base...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

Semi-supervised learning is proposed to exploit both labeled and unlabeled data. However, as the scale of data in real world applications increases significantly, conventional semisupervised algorithms usually lead to massive computational cost and cannot be applied to large scale datasets. In addition, label noise is usually present in the practical applications due to human annotation, which ...