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of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization

are highly cross-correlated, and principal components analysis implies that they are mostly driven by a single common factor. An important implication of this finding is that if any explanatory variables have been omitted, they are likely not firm-specific. We therefore re-run the regression, but 1 this time

improves over the known cross-feature analysis, CFA, technique) allowing solving anomaly detection problem in semi-supervised settings using well established supervised learning algorithms. Theoretical correctness of the proposed method is demonstrated. Empirical evaluation results on Android malware

Abstract -We present a technique for automatically assigning a neuroanatomical label to each location on a cortical surface model based on probabilistic information estimated from Introduction Techniques for labeling geometric features of the cerebral cortex are useful for analyzing a variety

This paper introduces Forensic Feature Extraction (FFE) and Cross-Drive Analysis (CDA), two new approaches for analyzing large data sets of disk images and other forensic data. FFE uses a variety of lexigraphic techniques for ex-tracting information from bulk data; CDA uses statistical techniques

of the Yamabe equation for the product conformal structure on SI(T) x S~-1(1), a circle of radius T crossed with a sphere of radius one. These exhibit interesting variational fea,tures uch a.s symmetry breaking and the existence of solutions with high Morse index. Since the time of the summer school

In the use of smoothing methods in data analysis, an important question is often: which observed features are "really there?", as opposed to being spurious sampling artifacts. An approach is described, based on scale space ideas that were originally developed in computer vision

In this paper, we perform a cross-linguistic study of prosodic features in sentence segmentation by using two different feature selection approaches: a forward search wrapper and feature filtering. Experiments in Arabic, English, and Mandarin show that prosodic features make significant

This paper develops a new methodology that makes use of the factor structure of large dimensional panels to understand the nature of non-stationarity in the data. We refer to it as PANIC – a ‘Panel Analysis of Non-stationarity in Idiosyncratic and Common components’. PANIC consists of univariate