In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (SVM). The SVM approach outperforms SVM in our experiments, with can be seen as a good methodology for rainfall satellite es...

Support Vector Machines (SVM) classifiers are applied to problem in Molecular Biology recognizing mitochondrial sеquences in the human genome. We present the results obtained by SVM hard classification, using the Plat’s model and Modified SVM outputs (MSVMO) method, an alternative way of interpreting and modifying the outputs of the SVM classifiers.

An automatic filtering system to classify individual unpleasant emotions represented by pupil-size changes was proposed. The support vector machines classifier was applied to single-trial data of pupil size and indicated the possibility of the correct judgment of individually unpleasant states immediately after looking at emotional pictures. The framework was then constructed to automatically f...

In this paper, we propose a framework of optimization method for classification problem and show that Support Vector Machine (SVM), Glover’s method and Shi’s Method (MCLP) are special cases of the model.

Abstract—In this paper, we apply a probabilistic method to predict the effect of quantization in a digital implementation of a Support Vector Machine (SVM). the quantization effects taken into consideration are both, input data and calculations done inside the processor. We derived a closed-form expression for these effects for an SVM using a 2 order polynomial Kernel and matched it with simula...

Statistical learning methods have been used in developing filters for predicting inhibitors of two P450 isoenzymes, CYP3A4 and CYP2D6. This work explores the use of different statistical learning methods for predicting inhibitors of these enzymes and an additional P450 enzyme, CYP2C9, and the substrates of the three P450 isoenzymes. Two consensus support vector machine (CSVM) methods, "positive...

We show that the orientation and location of the separating hyperplane for 2-class supervised pattern classiication 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...

A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions to the learning of decision surface. We cal...

Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. Firs...

This paper proposes to use a fuzzy entropy based feature selection approach to reduce the dimensionality of DAIS hyperspectral data. To compare its performance, three other filter based feature selection approaches were used. A support vector machine was used as a classification algorithm. In order to compare various feature selection approaches with full dataset, McNemar’s test based test for ...