Support vector machines (SVM) have been applied to build classifiers, which can help users make well-informed business decisions. Despite their high generalisation accuracy, the response time of SVM classifiers is still a concern when applied into real-time business intelligence systems, such as stock market surveillance and network intrusion detection. This paper speeds up the response of SVM ...

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...

We describe our work on Greek Named Entity Recognition using comparatively three different machine learning techniques: (i) Support Vector Machines (SVM), (ii) Maximum Entropy and (iii) Onetime, a shortcut method based on previous work of one of the authors. The majority of our system’s features use linguistic knowledge provided by: morphology, punctuation, position of the lexical units within ...

In this article, we compare decision trees (DT) and support vector machines (SVM) in classifying gene expressions. With the explosion of genome research, tremendous amount of data have been made available and a deep insight study becomes demanding. Among various kinds of gene analysis approaches being developed, sequence based gene expression classification shows the importance due to its abili...

Support vector machines with ramp loss ( $$L_r$$ -SVM) have attracted considerable attention due to the robustness of loss. However, corresponding optimization problem is non-convex, and given Karush–Kuhn–Tucker (KKT) conditions are only first-order necessary conditions. To enrich optimality theory -SVM, we first introduce analyze proximal operator for loss, then establish a stronger condition:...

In this paper, we introduce a new method for steganalysis of grey-scale images. First, we analyzed the effect of various steganographic processes on the statistical properties of the image. So we extracted the optimal features from the images, which have high ability in make differentiated between two groups of normal and stego images. In this method, high order statistics in discrete wavelet t...

Multi-label classification is the problem that classes are not mutually exclusive, so that an example may belong to more than one category. This poses challenges to the traditional pattern recognition theory where class overlap means classification error. Multi-label classification arises typically in semantic scene classification, text categorization, medical diagnosis, and bioinformatics. How...

-Land use classification is an important part of many remote-sensing applications. A lot of research has gone into the application of classifiers to remote-sensing images. Multi-spectral satellite imagery is an economical, precise and appropriate method of obtaining information on land use and land cover. In this paper, we have proposed an efficient technique for classifying the multispectral s...

Hypertext/text domains are characterized by several tens or hundreds of thousands of features. This represents a challenge for supervised learning algorithms which have to learn accurate classifiers using a small set of available training examples. In this paper, a fuzzy semi-supervised support vector machines (FSS-SVM) algorithm is proposed. It tries to overcome the need for a large labelled t...