This paper presents an unsupervised fuzzy-kernel learning vector quantization algorithm called FKLVQ. FKLVQ is a batch type of clustering learning network by fusing the batch learning, fuzzy membership functions, and kernel-induced distance measures. We compare FKLVQ with the wellknown fuzzy LVQ and the recently proposed fuzzy-soft LVQ on some artificial and real data sets. Experimental results...

We present a method to quantify the political legitimacy of a populace using public Twitter data. First, we represent the notion of legitimacy with respect to k-dimensional probabilistic topics, automatically culled from the politically oriented corpus. The short tweets are then converted to a feature vector in k-dimensional topic space. Leveraging sentiment analysis, we also consider the polar...

According to celebrated Hurwitz theorem, there exists four division algebras consisting of R (real numbers), C (complex numbers), H (quaternions) and O (octonions). Keeping in view the utility of octonion variable we have tried to extend the three dimensional vector analysis to seven dimensional one. Starting with the scalar and vector product in seven dimensions, we have redefined the gradient...

We show that the orientation and location of the separating hyperplane for 2-class supervised pattern classi cation obtained by the Support Vector Machine (SVM) proposed by Vapnik and his colleagues, is equivalent to the solution obtained by Fisher's Linear Discriminant on the set of Support Vectors. In other words, SVM can be seen as a way to \sparsify" Fisher's Linear Discriminant in order to...

This article deals with various aspects of normalization in the context of Support Vector Machines. We consider fist normalization of the vectors in the input space and point out the inherent limitations. A natural extension to the feature space is then represented by the kernel function normalization. A correction of the position of the Optimal Separating Hyperplane is subsequently introduced ...

In this paper we present the system we submitted to the WMT13 shared task on Quality Estimation. We participated in the Task 1.1. Each translated sentence is given a score between 0 and 1. The score is obtained by using several numerical or boolean features calculated according to the source and target sentences. We perform a linear regression of the feature space against scores in the range [0...

We propose a method that relates images and words. This method is based on statistical learning from image databases with words. The method uses two processes. The rst uniformly divides each image into sub-images. With this division, all words assigned to images are inherited by each sub-image. The second process clusters sub-images by vector quantization. These processes produce results which ...

this paper develops an analysis of budget deficit financing in termsof a crowding out or crowding in effect on the activity of the privatesector for the economies of iran and algeria as two mena countries,(because of its economic structures (dependence on oil revenue)) duringthe period 1970-2012 by using cointegration and vector errorcorrection approaches. the analysis confirms the existence of...

this paper is based on a combination of the principal component analysis (pca), eigenface and support vector machines. using n-fold method and with respect to the value of n, any person’s face images are divided into two sections. as a result, vectors of training features and test features are obtain ed. classification precision and accuracy was examined with three different types of kernel and...

the aim of this paper is to extend a hybrid protection plan for power transformer (pt) based on mra-ksir-ssvm. this paper offers a new scheme for protection of power transformers to distinguish internal faults from inrush currents. some significant characteristics of differential currents in the real pt operating circumstances are extracted. in this paper, multi resolution analysis (mra) is use...