Neural networks play an important role in system modelling. This is especially true if model building is mainly based on observed data. Among neural models the Support Vector Machine (SVM) solutions are attracting increasing attention, mostly because they automatically answer certain crucial questions involved by neural network construction. They derive an ‘optimal’ network structure and answer...

MOTIVATION The function of an unknown biological sequence can often be accurately inferred if we are able to map this unknown sequence to its corresponding homologous family. At present, discriminative methods such as SVM-Fisher and SVM-pairwise, which combine support vector machine (SVM) and sequence similarity, are recognized as the most accurate methods, with SVM-pairwise being the most accu...

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT(1A) selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-...

Template attacks and machine learning are two powerful methods in the field of side channel attacks. In this paper, we aimed to contribute to the novel application of support vector machine (SVM) algorithm in power analysis attacks. Especially, wavelet SVM can approximate arbitrary nonlinear functions due to the multidimensional analysis of wavelet functions and the generalization of SVM. Three...

In this paper the accuracy of two machine learning algorithms including SVM and Bayesian Network are investigated as two important algorithms in diagnosis of Parkinson’s disease. We use Parkinson's disease data in the University of California, Irvine (UCI). In order to optimize the SVM algorithm, different kernel functions and C parameters have been used and our results show that SVM with C par...

Machine learning techniques have facilitated image retrieval by automatically classifying and annotating images with keywords. Among them Support Vector Machines (SVMs) are used extensively due to their generalization properties. However, SVM training is notably a computationally intensive process especially when the training dataset is large. In this thesis distributed computing paradigms have...

This paper investigates a novel algorithm-EGA-SVM for text classification problem by combining support vector machines (SVM) with elitist genetic algorithm (GA). The new algorithm uses EGA, which is based on elite survival strategy, to optimize the parameters of SVM. Iris dataset and one hundred pieces of news reports in Chinese news are chosen to compare EGA-SVM, GA-SVM and traditional SVM. Th...

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum ...

The paper presents a novel learning algorithm for the class of L2 Support Vector Machines classifiers dubbed Direct L2 SVM. The proposed algorithm avoids solving the quadratic programming problem and yet, it produces both the same exact results as the classic quadratic programming based solution in a significantly shorter CPU time. The connections between various L2 SVM algorithms will be highl...